An Analysis of Tennis Data¶

By: Alan Zhang

To do list:

-- Explanation on technological improvements. See 3A)

-- Court differences 3A)

Table of Contents¶

Introduction
Data Collection
Exploratory Analysis

3A. Trends over time

Making a model (unsuccessfuly)

4a. Matrix Plot

4b. Backwards Selection

Conclusion and Final Thoughts

Introduction¶

-- Write intro here--

I did an analysis of ATP Tour matches to learn statistics, computer science, and to learn more about a sport I'm passionate about. I scraped data off of the internet, not downloading a single thing, and did statistical analysis on my favorite players. Working with the data has shown interesting trends over the history of tennis ---

2. Data Collection¶

We want to set up our data to look at specific statistics such as how stats differ for losers and winners on different courts, as well as statistics of top players over time.

Our code first sets up arrays to store these statistics, and then scrapes the data off of a publicly available webpage. I used this dataset: https://github.com/JeffSackmann/tennis_atp/blob/master/README.md

Since the dataset is stored as matches per year, I conglomerated all the years into one big dataset so I could analyze over time. While conglomerating, I extracted yearly averages of statistics I specifically wanted to look at.

# Necessary Imports
import pandas as pd
import datetime
import matplotlib.pyplot as plt
# Defining date parser for our data
def parse(t):
    ret = []
    for ts in t:
        try:
            string = str(ts)
            tsdt = datetime.date(int(string[:4]), int(string[4:6]), int(string[6:]))
        except TypeError:
            tsdt = datetime.date(1900,1,1)
        ret.append(tsdt)
    return ret

# Read in the first year of the data
df = pd.read_csv("https://raw.githubusercontent.com/JeffSackmann/tennis_atp/master/atp_matches_1968.csv", index_col=None,
                         header=0,
                         parse_dates=[5],
                         encoding = "ISO-8859-1",
                         date_parser=lambda t:parse(t))

# Create lists to store yearly aces of winners
yearlyAces = []
yearlyAces.append(df['w_ace'].mean())

# Create lists to store yearly aces of winners on different courts
yearlyAcesHard = []
yearlyAcesGrass = []
yearlyAcesClay = []

# Cut the dataframes on different courts
hardSurface = df.loc[df['surface'] == "Hard"]
grass = df.loc[df['surface'] == "Grass"]
clay = df.loc[df['surface'] == "Clay"]

# Add the first average yearly winners aces on different courts
yearlyAcesHard.append(hardSurface['w_ace'].mean())
yearlyAcesGrass.append(grass['w_ace'].mean())
yearlyAcesClay.append(clay['w_ace'].mean())

# Same thing for losers
yearlyLosersAces = []
yearlyLosersAces.append(df['l_ace'].mean())
hardSurface = df.loc[df['surface'] == "Hard"]
grass = df.loc[df['surface'] == "Grass"]
clay = df.loc[df['surface'] == "Clay"]

yearlyAcesHardL = []
yearlyAcesGrassL = []
yearlyAcesClayL = []
yearlyAcesHardL.append(hardSurface['l_ace'].mean())
yearlyAcesGrassL.append(grass['l_ace'].mean())
yearlyAcesClayL.append(clay['l_ace'].mean())

# Setting up data to look at the number of US players who finished in the top 4
USnumOfTop4 = []
top4 = df.loc[(df['round'] == "F") | (df['round'] == "SF") | (df['tourney_level'] == "G")]
USnumOfTop4.append(top4['winner_ioc'].value_counts()["USA"])

# Setting up data to look at the number of AUS, ESP, and FRA players who finished in the top 4
AUSnumOfTop4 = []
AUSnumOfTop4.append(top4['winner_ioc'].value_counts()["AUS"])
ESPnumOfTop4 = []
ESPnumOfTop4.append(top4['winner_ioc'].value_counts()["ESP"])
FRAnumOfTop4 = []
FRAnumOfTop4.append(top4['winner_ioc'].value_counts()["FRA"])


averageWinnersHeight = []
averageWinnersHeight.append(df['winner_ht'].mean())
averageLosersHeight = []
averageLosersHeight.append(df['loser_ht'].mean())

top8 = df.loc[(df['round'] == "F") | (df['round'] == "SF") | (df['tourney_level'] == "G") | (df['round'] == "QF")]
averageWinnersHeightTop8 = []
averageWinnersHeightTop8.append(top8['winner_ht'].mean())
averageLosersHeightTop8 = []
averageLosersHeightTop8.append(top8['loser_ht'].mean())

averageWinnersAgeTop8 = []
averageWinnersAgeTop8.append(top8['winner_age'].mean())
averageLosersAgeTop8 = []
averageLosersAgeTop8.append(top8['loser_age'].mean())

doubleFaultsW = []
doubleFaultsW.append(df['w_df'].mean())
doubleFaultsL = []
doubleFaultsL.append(df['l_df'].mean())

doubleFaultsWTop8 = []
doubleFaultsWTop8.append(top8['w_df'].mean())
doubleFaultsLTop8 =[]
doubleFaultsLTop8.append(top8['l_df'].mean())

for i in range(1969, 2022):
  url = "https://raw.githubusercontent.com/JeffSackmann/tennis_atp/master/atp_matches_" + str(i) + ".csv"
  df1 = pd.read_csv(url, index_col=None,
                         header=0,
                         parse_dates=[5],
                         encoding = "ISO-8859-1",
                         date_parser=lambda t:parse(t))
  df = pd.concat([df,df1])
  yearlyAces.append(df1['w_ace'].mean())
  hardSurface = df1.loc[df1['surface'] == "Hard"]
  grass = df1.loc[df1['surface'] == "Grass"]
  clay = df1.loc[df1['surface'] == "Clay"]

  yearlyAcesHard.append(hardSurface['w_ace'].mean())
  yearlyAcesGrass.append(grass['w_ace'].mean())
  yearlyAcesClay.append(clay['w_ace'].mean())

  yearlyLosersAces.append(df1['l_ace'].mean())
  yearlyAcesHardL.append(hardSurface['l_ace'].mean())
  yearlyAcesGrassL.append(grass['l_ace'].mean())
  yearlyAcesClayL.append(clay['l_ace'].mean())

  top4 = df1.loc[(df1['round'] == "F") | (df1['round'] == "SF") | (df1['tourney_level'] == "G")]
  USnumOfTop4.append(top4['winner_ioc'].value_counts()["USA"])
  AUSnumOfTop4.append(top4['winner_ioc'].value_counts()["AUS"])
  ESPnumOfTop4.append(top4['winner_ioc'].value_counts()["ESP"])
  FRAnumOfTop4.append(top4['winner_ioc'].value_counts()["FRA"])

  averageWinnersHeight.append(df1['winner_ht'].mean())
  averageLosersHeight.append(df1['loser_ht'].mean())

  top8 = df.loc[(df['round'] == "F") | (df['round'] == "SF") | (df['tourney_level'] == "G") | (df['round'] == "QF")]
  averageWinnersHeightTop8.append(top8['winner_ht'].mean())
  averageLosersHeightTop8.append(top8['loser_ht'].mean())

  averageWinnersAgeTop8.append(top8['winner_age'].mean())
  averageLosersAgeTop8.append(top8['loser_age'].mean())

  doubleFaultsW.append(df1['w_df'].mean())
  doubleFaultsL.append(df1['l_df'].mean())

  doubleFaultsWTop8.append(top8['w_df'].mean())
  doubleFaultsLTop8.append(top8['l_df'].mean())


combined = df
display(combined)

3a) Trends Over Time¶

After creating our big dataset and extracting info, here I graph the statsiscs I collected to observe some trends.

years = range(1968,2022)
plt.plot(years, yearlyLosersAces, label = "Total")
plt.xlabel("Years")
plt.ylabel("Average Number of Winner's Aces")
plt.title("Number of Winner's Aces Over Time")
plt.plot(years, yearlyAcesHard, label = "Hard")
plt.plot(years, yearlyAcesGrass, label = "Grass")
plt.plot(years, yearlyAcesClay, label = "Clay")
plt.legend()

<matplotlib.legend.Legend at 0x7f72a0f435d0>

This is a graph of the number of aces a winner scores over time on different courts. As we can see, the general trend is upwards. This could be attributed to a multitude of reasons.

One such reason would be equipment improvements ---- or skill at receiving lags behind faster serves because players need to recieve

Another trend we can see is that aces are much more common on grass, and more rare on clay courts. --- Insert some science behind why clay is better traction so people get to the ball faster?

One outlier to note is the spike at 2020. Due to covid, there was only 2 games played on grass that year, the spike at 2020 is not useful.

plt.plot(years, yearlyLosersAces, label = "Total")
plt.xlabel("Years")
plt.ylabel("Average Number of Loser's Aces")
plt.title("Number of Loser's Aces Over Time")
plt.plot(years, yearlyAcesHardL, label = "Hard")
plt.plot(years, yearlyAcesGrassL, label = "Grass")
plt.plot(years, yearlyAcesClayL, label = "Clay")
plt.legend()

<matplotlib.legend.Legend at 0x7f72a0a2ded0>

Here, we plot the same graph as above, but for the losing side of each match. We can see the same trends with courts and the number of aces going up, but the overall number of aces per match is lower than the winners which is to be expected.

Although this graph doesn't give us any new information it's reassuring to know that the data makes sense, losers are have worse stats than winners.

Next, I was interested in how strong the top regions were from around the world. I decided to measure this by seeing how many players from each top region made it to the top 4 in Grand Slam tournaments.

# Where good players come from over time
plt.plot(years, USnumOfTop4, label = 'US Players')
plt.plot(years, AUSnumOfTop4, label = 'Australian Players')
plt.plot(years, ESPnumOfTop4, label = 'Spain Players')
plt.plot(years, FRAnumOfTop4, label = 'France Players')
plt.title("Number of Players From Top regions Who made it to Top 4 in Grand Slams")

plt.xlabel("Years")
plt.ylabel("Number of Players At top 4 in Grand Slams")
plt.legend()

<matplotlib.legend.Legend at 0x7f72a1177c90>

Looking the graph, the trend is clear. The us used to dominate in early years, but nearing the turn of the century, France and Spain in particular overtook us in the number of athletes making it into top 4 at grand slams.

-- Insert speculation as to why here --

A general interest in sports is the physical makeup of top athelets. What makes a good basketball player? The obvious answer is height. I wondered this about tennis so I analyzed the height and weight of winners and losers of every match over time.

# Heights of Losers VS Winners Over Time
print(averageWinnersHeight)
plt.plot(years, averageWinnersHeight, label = 'Winners')
plt.plot(years, averageLosersHeight, label = 'Losers')
plt.title("Heights of Winners and Losers over Time")
plt.xlabel("Years")
plt.ylabel("Average Height (CM)")
plt.legend()

[182.34983498349834, 182.15227070347285, 181.89511400651466, 182.48958862366683, 182.32646212847555, 181.93656422379826, 181.70344053851906, 181.39985512495474, 181.4168490153173, 182.17356173238525, 182.17900063251108, 182.70902505282223, 182.55652911249294, 182.450014240957, 182.99580382900604, 183.14154302670624, 183.6393958464443, 183.61497005988025, 183.57448818897637, 183.43741007194245, 183.34635488308115, 183.34514549121292, 183.82739264664608, 184.22594954255615, 184.63892653835728, 184.82095038067735, 184.9247339735271, 185.04727370400215, 185.26058631921825, 185.03660612939842, 184.68287103231341, 184.7820710973725, 184.8089716203845, 184.17411545623835, 184.51627457299386, 184.44355098102284, 184.9592092877314, 184.59016393442624, 184.9471853257432, 185.17994195420832, 185.69418145956607, 185.92167110519307, 186.0180466373775, 186.3782371028357, 186.36488706365506, 187.03943661971832, 187.20372359470105, 187.46723343960326, 187.31355023239186, 187.1811567842852, 187.9256229081443, 187.6109022556391, 187.55882352941177, 188.09273182957392]

<matplotlib.legend.Legend at 0x7f72a10537d0>

Since I'm plotting the averages of many many matches, the differnce could be negligble, but the graph shows that winners are generally taller, which could lead some insight into height giving a slight advantage while playing tennis.

To narrow down the dataset a little and look at the peak of the sport, I decided to look at the heights of the Top 8 only to see if height mattered more or less at the highest level of competition.

# Heights of Losers VS Winners Over Time In Top 8
plt.plot(years, averageWinnersHeightTop8, label = 'Winners')
plt.plot(years, averageLosersHeightTop8, label = 'Losers')
plt.title("Heights of Winners and Losers in Top 8 over Time")

plt.xlabel("Years")
plt.ylabel("Average Height (CM)")
plt.legend()

<matplotlib.legend.Legend at 0x7f72a1074710>

-- Why this could be --

On a similar note, age has always played different factors in different sports. I wondered how much age mattered in tennis so I plotted it.

# Ages of Losers VS Winners Over Time
plt.plot(years, averageWinnersAgeTop8, label = 'Winners')
plt.plot(years, averageLosersAgeTop8, label = 'Losers')
plt.title("Ages of Winners and Losers over Time")

plt.xlabel("Years")
plt.ylabel("Average Age (Years)")
plt.legend()

<matplotlib.legend.Legend at 0x7f72a15e9290>

Although the graph looks like there is a trend, that players became younger, the age range is so small, most players being within 25.5 to 27 years old. This graph tells us very little.

-- Can find historical evidence of tennis being an old persons game --

Diving more into specific statistics, I was interested in the number of double faults over time, as I thought it should just get better over time as players become more consistent.

# Double Faults of Losers VS Winners Over Time
plt.plot(years, doubleFaultsW, label = 'Winners')
plt.plot(years, doubleFaultsL, label = 'Losers')
plt.title("Double Faults of Winners and Losers over Time")

plt.xlabel("Years")
plt.ylabel("Average Double Faults")
plt.legend()

<matplotlib.legend.Legend at 0x7f72a1731cd0>

There are three things to analyze here.

From the graph we can see that the losers distribution is the same as the winners but shifted up by 0.8.

The clear takeaway is that the losers line is shifted up; losers have more double faults, because double faults contribute to a loss.

An interesting and not so clear takeaway from this graph is why the loss distribution looks so similar to the win distribution. It could have something to do with the fact that we are comparing winners and losers, and with the way we conglomerated our data, a player can contribute to both the winners and losers average.

The last takeaway would come from analyzing the trend over time, why both losers and winners started serving more double faults around 1997, then trending down, and back up.

-- Speculation -- Around 2005-6 ish people wanted more consistent serves, not giving up free points. After acheiving consistency, people tried to gain speed back, playing more high risk high rewards serves, resulting in more double faults.

Since this trend was so interesting, I decided to take a closer look at the highest level of competition. Here is a graph of double faults of winners vs losers from top 8 matches.

# Double Faults of Losers VS Winners Over Time In top 8 Mathches
plt.plot(years, doubleFaultsWTop8, label = 'Winners')
plt.plot(years, doubleFaultsLTop8, label = 'Losers')
plt.title("Double Faults of Winners and Losers in Top 8 over Time")

plt.xlabel("Years")
plt.ylabel("Average Double Faults")
plt.legend()

<matplotlib.legend.Legend at 0x7f72a16d2a90>

From this graph the trend is even more exact. Losers of top 8 matches serve 0.8 to 0.9 more double faults than winners of these mathces. It is very confusing why it is so exact.

4a) Making a model¶

I decided to try and model what makes a good player by predicting player winrate from the available statistics. The first step in doing this is extracting each players's data from the match data.

listOfPlayers = combined['winner_name'].unique()

winCounts = combined['winner_name'].value_counts()
lossCounts = combined['loser_name'].value_counts()
winRates = []
for player in listOfPlayers:
  totalGames = winCounts.get(player, default = 0) + lossCounts.get(player, default = 0)
  # We filter out people who have played less than 100 games
  if totalGames < 100:
    continue
  else:
    wins = combined.loc[(combined['winner_name'] ==  player)]
    winAvgs = wins.mean()
    losses = combined.loc[(combined['loser_name'] ==  player)]
    lossAvgs = losses.mean()
    winRates.append((player, winCounts.get(player, default = 0)/totalGames, winAvgs['minutes'], winAvgs['w_ace'], winAvgs['w_df'], winAvgs['w_svpt'] \
                     , winAvgs['w_1stIn'], winAvgs['w_1stWon'], winAvgs['w_2ndWon'], winAvgs['w_SvGms'], winAvgs['w_bpSaved'], winAvgs['w_bpFaced'], lossAvgs['minutes']\
                     , lossAvgs['l_ace'], lossAvgs['l_df'], lossAvgs['l_svpt'], lossAvgs['l_1stIn'], lossAvgs['l_1stWon'], lossAvgs['l_2ndWon'], lossAvgs['l_SvGms']\
                     , lossAvgs['l_bpSaved'], lossAvgs['l_bpFaced']))

print(winRates)

/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:13: FutureWarning: DataFrame.mean and DataFrame.median with numeric_only=None will include datetime64 and datetime64tz columns in a future version.
  del sys.path[0]
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:13: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.
  del sys.path[0]
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:15: FutureWarning: DataFrame.mean and DataFrame.median with numeric_only=None will include datetime64 and datetime64tz columns in a future version.
  from ipykernel import kernelapp as app
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:15: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.
  from ipykernel import kernelapp as app

[('Tom Okker', 0.7023809523809523, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Tom Edlefsen', 0.390625, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Mark Cox', 0.5823817292006526, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('John Bartlett', 0.24390243902439024, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Rod Laver', 0.8013136288998358, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Thomaz Koch', 0.546875, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Roy Emerson', 0.6274509803921569, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Andres Gimeno', 0.670926517571885, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Guillermo Vilas', 0.7616747181964574, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 138.0, 0.0, 2.5, 96.0, 67.5, 40.5, 14.5, 15.0, 3.5, 8.5), ('Jaime Pinto Bravo', 0.40487804878048783, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Julian Ganzabal', 0.45454545454545453, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Patricio Cornejo', 0.40634005763688763, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Edison Mandarino', 0.5588235294117647, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Jan Kodes', 0.6354883081155434, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Fred Stolle', 0.5376344086021505, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Raymond Moore', 0.48830409356725146, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Robert Maud', 0.42592592592592593, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Clark Graebner', 0.6550802139037433, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Phil Dent', 0.538781163434903, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Owen Davidson', 0.44324324324324327, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Bill Bowrey', 0.4624277456647399, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Ove Nils Bengtson', 0.40893470790378006, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Gerald Battrick', 0.4393939393939394, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Dick Crealy', 0.5065359477124183, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Marty Riessen', 0.6143884892086331, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Alex Metreveli', 0.7158176943699732, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Roger Taylor', 0.5862068965517241, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Cliff Drysdale', 0.6230636833046471, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Charlie Pasarell', 0.5050709939148073, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Nikola Pilic', 0.5732484076433121, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Torben Ulrich', 0.2716049382716049, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Dennis Ralston', 0.5392670157068062, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('John Newcombe', 0.7352517985611511, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Boro Jovanovic', 0.3984375, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Istvan Gulyas', 0.5145631067961165, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Pierre Barthes', 0.528052805280528, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Mal Anderson', 0.6097560975609756, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Hans Jurgen Pohmann', 0.5061224489795918, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Hans Joachim Ploetz', 0.36363636363636365, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Barry Phillips Moore', 0.46359223300970875, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Onny Parun', 0.5324858757062146, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Martin Mulligan', 0.58, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Jurgen Fassbender', 0.5201149425287356, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Richard Pancho 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nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Fritz Buehning', 0.4851063829787234, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Jay Lapidus', 0.4175824175824176, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Jan Gunnarsson', 0.48346055979643765, 90.16666666666667, 5.777777777777778, 2.0, 74.44444444444444, 37.05555555555556, 28.0, 19.055555555555557, 11.61111111111111, 3.888888888888889, 6.222222222222222, 81.96153846153847, 4.038461538461538, 3.3461538461538463, 69.76923076923077, 32.61538461538461, 21.73076923076923, 15.423076923076923, 10.461538461538462, 4.8076923076923075, 9.346153846153847), ('Larry Stefanki', 0.37857142857142856, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Roland Stadler', 0.425, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Hans Simonsson', 0.3620689655172414, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('John Fitzgerald', 0.5095137420718816, 96.41176470588235, 2.6315789473684212, 3.736842105263158, 78.73684210526316, 49.0, 36.26315789473684, 16.526315789473685, 12.263157894736842, 3.3684210526315788, 5.0, 87.8, 1.7391304347826086, 4.3478260869565215, 76.34782608695652, 46.869565217391305, 29.217391304347824, 12.130434782608695, 11.304347826086957, 5.173913043478261, 9.956521739130435), ('Ricardo Acuna', 0.39361702127659576, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Pedro Rebolledo', 0.4889867841409692, 70.0, 0.0, 0.0, 44.0, 18.0, 12.0, 14.0, 7.0, 3.0, 5.0, 96.0, 0.2, 1.6, 74.7, 53.9, 28.7, 8.0, 10.5, 5.0, 10.4), ('Peter Rennert', 0.44954128440366975, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Eric Fromm', 0.3805970149253731, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Mel Purcell', 0.5380281690140845, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Thomas Hogstedt', 0.37037037037037035, 107.29411764705883, 4.833333333333333, 2.9444444444444446, 72.33333333333333, 39.333333333333336, 28.944444444444443, 19.0, 11.61111111111111, 3.2777777777777777, 4.944444444444445, 93.08571428571429, 4.0, 4.114285714285714, 69.17142857142858, 34.82857142857143, 22.6, 14.228571428571428, 10.285714285714286, 4.2, 8.628571428571428), ('Lloyd Bourne', 0.4276729559748428, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Diego Perez', 0.490280777537797, 117.82352941176471, 2.8823529411764706, 1.6470588235294117, 78.0, 51.11764705882353, 34.35294117647059, 15.117647058823529, 12.176470588235293, 3.3529411764705883, 6.0588235294117645, 113.29032258064517, 1.9696969696969697, 3.393939393939394, 82.57575757575758, 48.0, 28.03030303030303, 15.666666666666666, 12.0, 5.515151515151516, 10.727272727272727), ('Marko Ostoja', 0.3826086956521739, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Wally Masur', 0.5349593495934959, 102.81553398058253, 5.192660550458716, 2.0091743119266057, 82.42201834862385, 55.51376146788991, 40.51376146788991, 15.155963302752294, 12.963302752293577, 3.688073394495413, 5.36697247706422, 102.82222222222222, 4.46875, 2.4479166666666665, 84.1875, 54.729166666666664, 36.71875, 14.479166666666666, 12.958333333333334, 4.260416666666667, 7.708333333333333), ('Kelly Evernden', 0.4823943661971831, 90.0, 4.4, 1.2, 63.6, 36.1, 28.0, 16.8, 10.4, 2.8, 3.7, 112.84210526315789, 3.0, 2.263157894736842, 82.10526315789474, 48.1578947368421, 31.263157894736842, 17.05263157894737, 12.526315789473685, 4.631578947368421, 8.368421052631579), ('Marcos Hocevar', 0.4603174603174603, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Thierry Tulasne', 0.5099778270509978, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 90.0, 1.6666666666666667, 5.333333333333333, 71.66666666666667, 39.666666666666664, 24.333333333333332, 11.333333333333334, 9.333333333333334, 6.333333333333333, 11.333333333333334), ('Mats Wilander', 0.7178841309823678, 102.65306122448979, 3.690909090909091, 2.272727272727273, 84.12727272727273, 55.981818181818184, 40.09090909090909, 14.472727272727273, 13.0, 4.345454545454546, 6.636363636363637, 97.67213114754098, 2.388059701492537, 2.253731343283582, 78.91044776119404, 50.53731343283582, 31.313432835820894, 11.985074626865671, 11.597014925373134, 5.298507462686567, 9.835820895522389), ('Jerome Potier', 0.3698630136986301, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Sammy Giammalva Jr', 0.4923547400611621, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Matt Doyle', 0.45714285714285713, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Shahar Perkiss', 0.46017699115044247, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 113.0, 3.0, 0.0, 84.0, 38.0, 27.5, 19.5, 10.5, 6.5, 10.0), ('Tom Cain', 0.39090909090909093, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Jimmy Arias', 0.5636007827788649, 100.51428571428572, 2.057142857142857, 2.914285714285714, 77.57142857142857, 55.114285714285714, 37.25714285714286, 12.028571428571428, 12.028571428571428, 4.2, 6.6, 99.69444444444444, 1.7777777777777777, 2.861111111111111, 73.88888888888889, 49.52777777777778, 28.916666666666668, 11.0, 10.972222222222221, 5.388888888888889, 9.944444444444445), ('Marcel Freeman', 0.37857142857142856, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Pablo Arraya', 0.4986072423398329, 120.1875, 4.0, 2.1875, 83.8125, 53.9375, 37.6875, 15.5625, 12.5625, 4.4375, 6.875, 108.89473684210526, 3.263157894736842, 3.3157894736842106, 77.57894736842105, 45.26315789473684, 27.63157894736842, 14.421052631578947, 11.789473684210526, 4.526315789473684, 9.105263157894736), ('Florin Segarceanu', 0.4338235294117647, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Tim Mayotte', 0.6256880733944954, 104.33333333333333, 8.25, 5.416666666666667, 83.83333333333333, 47.666666666666664, 37.166666666666664, 18.666666666666668, 13.166666666666666, 3.8333333333333335, 5.666666666666667, 107.92857142857143, 5.857142857142857, 6.785714285714286, 86.07142857142857, 45.357142857142854, 31.928571428571427, 18.0, 12.928571428571429, 5.071428571428571, 9.285714285714286), ('Anders Jarryd', 0.6033434650455927, 87.6842105263158, 3.551282051282051, 2.641025641025641, 66.84615384615384, 38.64102564102564, 29.833333333333332, 16.012820512820515, 10.923076923076923, 2.6923076923076925, 3.948717948717949, 99.36046511627907, 3.588888888888889, 4.533333333333333, 77.1, 39.9, 26.9, 16.744444444444444, 11.766666666666667, 4.322222222222222, 8.466666666666667), ('Chip Hooper', 0.4663677130044843, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Peter Doohan', 0.3787878787878788, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 96.0, 4.0, 6.0, 82.5, 54.5, 34.0, 15.0, 13.0, 2.5, 6.0), ('Zoltan Kuharszky', 0.37606837606837606, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Claudio Panatta', 0.4251012145748988, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Marty Davis', 0.46932515337423314, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Juan Aguilera', 0.5104166666666666, 107.0, 2.5, 1.75, 66.0, 37.0, 29.0, 16.0, 11.75, 1.5, 3.25, 88.71428571428571, 1.5714285714285714, 3.142857142857143, 64.35714285714286, 36.857142857142854, 21.571428571428573, 10.214285714285714, 9.571428571428571, 4.928571428571429, 9.857142857142858), ('Roberto Arguello', 0.4206896551724138, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Joakim Nystrom', 0.6479217603911981, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Mark Dickson', 0.5203252032520326, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Eduardo Bengoechea', 0.4681818181818182, 139.66666666666666, 1.3333333333333333, 1.3333333333333333, 97.66666666666667, 73.33333333333333, 44.0, 12.0, 14.666666666666666, 8.333333333333334, 13.0, 128.0, 0.6666666666666666, 2.6666666666666665, 90.66666666666667, 58.666666666666664, 38.0, 13.0, 13.333333333333334, 4.0, 8.666666666666666), ('Ben Testerman', 0.4603174603174603, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Nduka Odizor', 0.42857142857142855, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 94.33333333333333, 4.333333333333333, 5.666666666666667, 65.66666666666667, 33.0, 25.0, 14.666666666666666, 11.333333333333334, 1.6666666666666667, 4.666666666666667), ('Jimmy Brown', 0.4426751592356688, 133.0, 2.5, 0.75, 94.75, 82.0, 51.75, 7.0, 14.5, 7.25, 10.25, 96.75, 0.5833333333333334, 1.5, 67.66666666666667, 50.583333333333336, 26.666666666666668, 7.75, 10.583333333333334, 2.75, 7.583333333333333), ('Jim Gurfein', 0.3786407766990291, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Brad Gilbert', 0.6435643564356436, 109.13675213675214, 6.2682926829268295, 3.1951219512195124, 81.6910569105691, 46.61788617886179, 35.08130081300813, 18.170731707317074, 12.59349593495935, 4.105691056910569, 6.170731707317073, 109.19565217391305, 4.515789473684211, 4.273684210526316, 82.54736842105264, 45.589473684210525, 31.13684210526316, 15.736842105263158, 12.284210526315789, 5.315789473684211, 9.526315789473685), ('Pat Cash', 0.6139896373056994, 102.45833333333333, 6.375, 3.1666666666666665, 76.625, 46.041666666666664, 35.125, 16.833333333333332, 12.083333333333334, 3.3333333333333335, 4.75, 119.52941176470588, 5.676470588235294, 4.5588235294117645, 94.58823529411765, 53.8235294117647, 37.05882352941177, 17.794117647058822, 13.470588235294118, 5.823529411764706, 10.058823529411764), ('Henri Leconte', 0.5833333333333334, 100.34545454545454, 6.913793103448276, 4.241379310344827, 81.32758620689656, 42.37931034482759, 33.08620689655172, 21.43103448275862, 12.431034482758621, 3.793103448275862, 5.275862068965517, 102.81818181818181, 5.457142857142857, 4.957142857142857, 86.77142857142857, 41.82857142857143, 29.542857142857144, 20.685714285714287, 13.057142857142857, 5.3428571428571425, 9.471428571428572), ('Jeremy Bates', 0.40372670807453415, 100.33333333333333, 3.76, 3.68, 81.88, 47.1, 35.62, 17.6, 12.4, 3.78, 5.56, 88.13432835820896, 3.22972972972973, 4.108108108108108, 76.33783783783784, 41.445945945945944, 27.364864864864863, 15.054054054054054, 11.378378378378379, 4.6891891891891895, 8.85135135135135), ('Mike Leach', 0.42045454545454547, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Francesco Cancellotti', 0.49572649572649574, 75.0, 1.0, 0.0, 63.0, 44.0, 34.0, 8.0, 9.0, 3.0, 3.0, 83.33333333333333, 1.3333333333333333, 1.3333333333333333, 58.333333333333336, 38.0, 20.333333333333332, 9.0, 10.333333333333334, 3.6666666666666665, 9.0), ('Scott Davis', 0.5099009900990099, 105.58333333333333, 3.4166666666666665, 3.5, 85.41666666666667, 52.333333333333336, 38.833333333333336, 17.666666666666668, 13.083333333333334, 2.9166666666666665, 4.75, 100.04, 3.36, 6.36, 82.28, 44.48, 29.52, 16.28, 12.16, 4.28, 8.52), ('Alejandro Ganzabal', 0.4, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Sergio Casal', 0.45787545787545786, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 116.0, 3.0, 1.75, 87.25, 54.0, 35.0, 14.25, 14.0, 2.75, 8.0), ('Mike Bauer', 0.45180722891566266, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 92.0, 1.0, 8.0, 78.0, 41.0, 25.0, 13.0, 12.0, 8.0, 14.0), ('Danie Visser', 0.416289592760181, 136.0, 4.0, 2.0, 114.0, 76.0, 49.0, 24.0, 15.0, 13.0, 14.0, 108.5, 9.0, 1.0, 84.5, 54.5, 38.5, 13.5, 12.5, 3.0, 5.5), ('Martin Wostenholme', 0.3916083916083916, 91.66666666666667, 1.6666666666666667, 1.6666666666666667, 65.83333333333333, 43.5, 30.166666666666668, 10.666666666666666, 10.5, 3.5, 6.166666666666667, 106.375, 2.125, 3.25, 79.875, 55.875, 33.375, 9.875, 11.875, 4.375, 9.0), ('Alberto Tous', 0.4019607843137255, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Juan Avendano', 0.3418803418803419, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Bruce Derlin', 0.3564356435643564, 77.0, 1.0, 0.0, 45.0, 35.0, 21.0, 3.0, 8.0, 0.0, 3.0, 96.0, 0.0, 0.5, 80.0, 58.5, 29.0, 8.5, 11.5, 6.0, 13.0), ('Matt Anger', 0.43617021276595747, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Henrik Sundstrom', 0.5877862595419847, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Broderick Dyke', 0.39195979899497485, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 135.2, 3.0, 6.5, 100.16666666666667, 58.166666666666664, 36.5, 19.166666666666668, 14.666666666666666, 6.333333333333333, 11.166666666666666), ('Guy Forget', 0.5669642857142857, 105.09150326797386, 9.329268292682928, 1.829268292682927, 81.0060975609756, 47.609756097560975, 38.40853658536585, 18.5609756097561, 12.951219512195122, 3.0853658536585367, 4.432926829268292, 108.61682242990655, 8.447368421052632, 3.0, 87.41228070175438, 49.921052631578945, 36.89473684210526, 17.49122807017544, 13.157894736842104, 4.5964912280701755, 7.754385964912281), ('Martin Jaite', 0.6270833333333333, 113.83333333333333, 5.404761904761905, 2.1904761904761907, 78.42857142857143, 41.0, 30.0, 20.11904761904762, 12.095238095238095, 3.9047619047619047, 6.166666666666667, 104.11627906976744, 3.627906976744186, 2.6511627906976742, 72.97674418604652, 37.30232558139535, 24.186046511627907, 16.53488372093023, 11.116279069767442, 4.1395348837209305, 8.13953488372093), ('Michael Westphal', 0.4484536082474227, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Jakob Hlasek', 0.5692108667529108, 95.47651006711409, 6.630573248407643, 3.127388535031847, 79.88535031847134, 47.904458598726116, 37.06369426751592, 16.955414012738853, 12.471337579617835, 3.43312101910828, 5.006369426751593, 93.48550724637681, 5.219178082191781, 3.8904109589041096, 80.65753424657534, 47.24657534246575, 33.178082191780824, 14.712328767123287, 12.150684931506849, 4.36986301369863, 7.972602739726027), ('Glenn Michibata', 0.37948717948717947, 115.2, 4.2, 4.0, 88.6, 54.6, 39.4, 19.8, 14.0, 3.6, 5.2, 82.71428571428571, 1.7142857142857142, 2.7142857142857144, 61.714285714285715, 33.57142857142857, 22.428571428571427, 13.0, 10.0, 3.857142857142857, 7.714285714285714), ('John Frawley', 0.43670886075949367, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Jonathan Canter', 0.4146341463414634, 100.0, 1.0, 2.5, 77.5, 38.0, 28.5, 23.5, 13.0, 1.5, 2.5, 102.6, 3.3333333333333335, 1.8333333333333333, 93.66666666666667, 51.0, 34.666666666666664, 19.166666666666668, 13.333333333333334, 5.333333333333333, 9.5), ('Mike De Palmer', 0.4720496894409938, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Simon Youl', 0.3974358974358974, 93.88, 4.08, 2.4, 75.36, 45.92, 34.48, 16.12, 11.68, 3.52, 4.92, 100.13333333333334, 4.416666666666667, 3.7916666666666665, 84.54166666666667, 50.479166666666664, 34.479166666666664, 15.416666666666666, 12.770833333333334, 5.104166666666667, 9.083333333333334), ('David Pate', 0.5186246418338109, 92.76923076923077, 5.769230769230769, 2.6923076923076925, 75.0, 46.30769230769231, 34.0, 15.615384615384615, 11.846153846153847, 3.1538461538461537, 4.923076923076923, 93.84615384615384, 5.153846153846154, 3.6538461538461537, 79.76923076923077, 48.42307692307692, 31.346153846153847, 13.692307692307692, 11.884615384615385, 5.423076923076923, 9.576923076923077), ('Slobodan Zivojinovic', 0.5236486486486487, 109.66666666666667, 15.666666666666666, 1.6666666666666667, 84.0, 49.0, 40.333333333333336, 21.0, 13.333333333333334, 5.666666666666667, 6.0, 100.0, 10.333333333333334, 4.0, 82.33333333333333, 46.0, 33.5, 19.0, 12.166666666666666, 2.8333333333333335, 5.166666666666667), ('Michiel Schapers', 0.47191011235955055, 110.61538461538461, 4.769230769230769, 2.9615384615384617, 84.6923076923077, 58.65384615384615, 44.42307692307692, 14.5, 13.884615384615385, 3.5, 4.923076923076923, 104.63157894736842, 3.358974358974359, 3.4871794871794872, 82.1025641025641, 54.05128205128205, 35.38461538461539, 12.666666666666666, 12.205128205128204, 4.435897435897436, 8.025641025641026), ('Todd Nelson', 0.3706293706293706, 111.66666666666667, 7.5, 7.25, 108.0, 62.5, 48.5, 22.25, 16.5, 3.25, 5.5, 101.5, 3.857142857142857, 4.428571428571429, 83.14285714285714, 47.42857142857143, 32.142857142857146, 14.571428571428571, 12.0, 5.285714285714286, 9.285714285714286), ('Libor Pimek', 0.5101214574898786, 69.0, 0.0, 1.0, 43.0, 26.0, 17.0, 11.0, 8.0, 0.0, 2.0, 80.6, 1.6, 7.4, 64.0, 33.8, 21.2, 11.6, 9.4, 4.0, 8.8), ('Paul Annacone', 0.5498281786941581, 142.33333333333334, 7.666666666666667, 4.666666666666667, 117.66666666666667, 66.33333333333333, 48.333333333333336, 25.0, 16.666666666666668, 7.333333333333333, 10.666666666666666, 92.71428571428571, 8.428571428571429, 7.428571428571429, 80.57142857142857, 43.857142857142854, 30.857142857142858, 16.571428571428573, 11.857142857142858, 5.0, 8.571428571428571), ('Stefan Edberg', 0.7465181058495822, 98.59197324414716, 3.2476780185758516, 3.3560371517027865, 76.45510835913312, 47.343653250774, 35.656346749226, 15.860681114551083, 12.040247678018575, 3.458204334365325, 4.9907120743034055, 108.03846153846153, 3.0689655172413794, 4.706896551724138, 90.36206896551724, 54.810344827586206, 37.060344827586206, 16.232758620689655, 13.474137931034482, 4.801724137931035, 8.732758620689655), ('Johan Carlsson', 0.4166666666666667, 122.66666666666667, 1.0, 2.5555555555555554, 91.22222222222223, 59.333333333333336, 39.666666666666664, 17.666666666666668, 13.666666666666666, 4.444444444444445, 7.555555555555555, 95.78571428571429, 1.5714285714285714, 2.2857142857142856, 70.71428571428571, 47.0, 27.357142857142858, 10.5, 10.142857142857142, 4.5, 8.857142857142858), ('Hans Schwaier', 0.4105960264900662, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Miloslav Mecir', 0.6796875, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Claudio Mezzadri', 0.45703125, 109.76666666666667, 3.4, 2.3666666666666667, 75.03333333333333, 43.5, 31.6, 18.0, 11.9, 3.533333333333333, 5.366666666666666, 108.46666666666667, 2.4444444444444446, 3.6666666666666665, 77.88888888888889, 42.31111111111111, 26.77777777777778, 15.911111111111111, 11.755555555555556, 5.6, 10.155555555555555), ('Robert Seguso', 0.49162011173184356, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 90.0, 4.0, 6.0, 79.0, 41.0, 29.0, 21.0, 14.0, 2.0, 5.0), ('Ken Flach', 0.3939393939393939, 106.0, 12.0, 3.0, 89.0, 56.0, 43.0, 14.0, 14.0, 7.0, 10.0, 111.4, 7.6, 4.6, 98.2, 55.0, 39.6, 19.6, 14.2, 4.0, 7.4), ('Aaron Krickstein', 0.6049004594180705, 101.94067796610169, 4.961240310077519, 2.007751937984496, 84.97674418604652, 49.883720930232556, 36.18604651162791, 19.25581395348837, 12.953488372093023, 4.232558139534884, 6.457364341085271, 93.6421052631579, 3.4134615384615383, 2.576923076923077, 81.6923076923077, 47.76923076923077, 30.346153846153847, 15.35576923076923, 11.951923076923077, 5.221153846153846, 9.51923076923077), ('Amos Mansdorf', 0.5701107011070111, 91.5047619047619, 4.247787610619469, 1.7256637168141593, 74.95575221238938, 43.50442477876106, 31.893805309734514, 17.95575221238938, 11.761061946902656, 3.5486725663716814, 5.274336283185841, 98.84782608695652, 3.3636363636363638, 2.878787878787879, 83.06060606060606, 47.35353535353536, 31.272727272727273, 16.747474747474747, 12.222222222222221, 5.0606060606060606, 8.828282828282829), ('Eric Jelen', 0.4963235294117647, 95.6923076923077, 1.6153846153846154, 2.3846153846153846, 82.76923076923077, 57.30769230769231, 37.38461538461539, 13.461538461538462, 12.307692307692308, 3.6153846153846154, 6.384615384615385, 87.86363636363636, 1.2727272727272727, 3.090909090909091, 75.54545454545455, 51.59090909090909, 30.818181818181817, 10.227272727272727, 10.818181818181818, 5.454545454545454, 9.454545454545455), ('Jonas Svensson', 0.5612903225806452, 101.58227848101266, 3.317073170731707, 1.7682926829268293, 79.2560975609756, 51.30487804878049, 36.47560975609756, 14.75609756097561, 12.097560975609756, 4.158536585365853, 6.2073170731707314, 104.91666666666667, 2.5, 2.227272727272727, 82.17045454545455, 52.93181818181818, 32.45454545454545, 13.159090909090908, 12.022727272727273, 5.568181818181818, 9.965909090909092), ('Leif Shiras', 0.40804597701149425, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Todd Witsken', 0.46, 105.10714285714286, 2.533333333333333, 2.6666666666666665, 80.16666666666667, 48.266666666666666, 33.3, 17.1, 12.1, 4.1, 6.466666666666667, 107.14285714285714, 2.4186046511627906, 3.9069767441860463, 78.13953488372093, 46.93023255813954, 29.767441860465116, 13.511627906976743, 12.046511627906977, 3.813953488372093, 8.418604651162791), ('Peter Lundgren', 0.4666666666666667, 92.22222222222223, 5.333333333333333, 4.0, 76.77777777777777, 41.72222222222222, 31.444444444444443, 19.22222222222222, 12.11111111111111, 4.111111111111111, 5.888888888888889, 93.3157894736842, 4.846153846153846, 5.051282051282051, 78.0, 39.97435897435897, 26.46153846153846, 17.666666666666668, 11.743589743589743, 4.384615384615385, 8.256410256410257), ('Tarik Benhabiles', 0.39204545454545453, 108.75, 6.5, 4.0, 80.5, 39.0, 28.75, 21.75, 12.25, 6.0, 8.5, 99.0, 3.5833333333333335, 2.5833333333333335, 74.25, 40.083333333333336, 26.0, 14.25, 10.916666666666666, 3.9166666666666665, 8.25), ('Mark Kratzmann', 0.38461538461538464, 109.8, 4.0, 3.0, 85.8, 48.6, 36.8, 20.4, 13.4, 3.6, 6.0, 115.92857142857143, 3.7142857142857144, 3.7142857142857144, 92.57142857142857, 48.92857142857143, 34.0, 22.5, 13.428571428571429, 4.5, 7.785714285714286), ('Paolo Cane', 0.5074626865671642, 100.375, 6.291666666666667, 1.5416666666666667, 72.5, 39.541666666666664, 29.708333333333332, 17.75, 11.583333333333334, 2.9166666666666665, 4.708333333333333, 102.91176470588235, 4.529411764705882, 2.588235294117647, 77.52941176470588, 37.61764705882353, 25.529411764705884, 18.529411764705884, 11.529411764705882, 5.264705882352941, 9.147058823529411), ('Goran Prpic', 0.5101214574898786, 105.78125, 2.40625, 2.1875, 77.265625, 55.046875, 37.640625, 11.625, 12.1875, 3.6875, 6.171875, 107.27868852459017, 2.4444444444444446, 2.873015873015873, 79.88888888888889, 57.301587301587304, 34.04761904761905, 10.015873015873016, 12.031746031746032, 4.936507936507937, 9.603174603174603), ('Emilio Sanchez', 0.5972413793103448, 107.15827338129496, 4.204225352112676, 1.1126760563380282, 75.70422535211267, 49.852112676056336, 36.11267605633803, 14.330985915492958, 11.830985915492958, 3.507042253521127, 5.197183098591549, 104.24615384615385, 2.847826086956522, 1.4492753623188406, 79.65217391304348, 50.67391304347826, 32.05072463768116, 13.391304347826088, 11.869565217391305, 4.934782608695652, 9.021739130434783), ('Karel Novacek', 0.5494505494505495, 99.29824561403508, 6.790055248618785, 2.0607734806629834, 78.4696132596685, 44.552486187845304, 34.28176795580111, 18.138121546961326, 12.248618784530386, 3.701657458563536, 5.419889502762431, 98.7983193277311, 5.3858267716535435, 2.6141732283464565, 80.58267716535433, 46.04724409448819, 31.52755905511811, 15.606299212598426, 12.0, 4.732283464566929, 8.4251968503937), ('Boris Becker', 0.7608926673751328, 107.0863309352518, 10.645901639344263, 4.531147540983606, 82.71147540983607, 47.924590163934425, 38.97377049180328, 18.288524590163934, 12.885245901639344, 3.1180327868852458, 4.426229508196721, 118.14814814814815, 9.867256637168142, 5.592920353982301, 92.61061946902655, 51.84070796460177, 38.663716814159294, 18.20353982300885, 13.646017699115044, 4.743362831858407, 8.079646017699115), ('Kent Carlsson', 0.7339449541284404, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Derrick Rostagno', 0.5106382978723404, 102.51724137931035, 6.377777777777778, 3.1555555555555554, 81.55555555555556, 50.888888888888886, 38.022222222222226, 15.844444444444445, 12.71111111111111, 3.7111111111111112, 5.555555555555555, 97.68354430379746, 4.780487804878049, 3.841463414634146, 83.40243902439025, 50.0, 33.26829268292683, 14.487804878048781, 12.21951219512195, 4.914634146341464, 9.0), ('Christo Van Rensburg', 0.50920245398773, 111.28888888888889, 4.956521739130435, 6.913043478260869, 82.93478260869566, 48.32608695652174, 36.56521739130435, 17.91304347826087, 12.608695652173912, 4.065217391304348, 6.043478260869565, 108.1, 3.15, 6.766666666666667, 78.8, 45.31666666666667, 30.416666666666668, 14.633333333333333, 12.15, 4.666666666666667, 8.816666666666666), ('Horacio De La Pena', 0.5135135135135135, 97.45652173913044, 1.2608695652173914, 1.173913043478261, 65.15217391304348, 46.21739130434783, 31.130434782608695, 10.956521739130435, 10.217391304347826, 3.239130434782609, 4.913043478260869, 103.67213114754098, 1.1451612903225807, 1.3225806451612903, 75.12903225806451, 51.32258064516129, 28.70967741935484, 10.5, 11.129032258064516, 5.032258064516129, 10.064516129032258), ('Francisco Maciel', 0.4672897196261682, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Glenn Layendecker', 0.425414364640884, 117.125, 8.375, 4.75, 97.5, 60.375, 45.75, 19.875, 15.125, 4.75, 6.75, 105.16666666666667, 4.083333333333333, 3.6666666666666665, 86.75, 51.666666666666664, 33.833333333333336, 16.0, 13.25, 6.083333333333333, 10.333333333333334), ('Dan Cassidy', 0.3181818181818182, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Thomas Muster', 0.6928176795580111, 95.77020202020202, 3.084705882352941, 2.2494117647058824, 74.12705882352941, 45.19529411764706, 32.56470588235294, 16.105882352941176, 11.578823529411764, 3.7105882352941175, 5.503529411764706, 98.3416149068323, 2.9529411764705884, 2.7058823529411766, 81.96470588235294, 51.84705882352941, 32.529411764705884, 14.758823529411766, 12.294117647058824, 5.08235294117647, 9.047058823529412), ('Christian Miniussi', 0.41843971631205673, 105.92, 2.36, 2.32, 74.76, 46.0, 32.96, 14.52, 11.84, 3.76, 6.2, 112.57575757575758, 2.8484848484848486, 3.8484848484848486, 86.48484848484848, 49.303030303030305, 31.333333333333332, 17.181818181818183, 12.666666666666666, 5.090909090909091, 9.787878787878787), ('Jordi Arrese', 0.5172413793103449, 114.55, 1.2479338842975207, 1.9090909090909092, 77.53719008264463, 56.603305785123965, 36.97520661157025, 10.84297520661157, 12.016528925619834, 4.264462809917355, 7.090909090909091, 106.33333333333333, 0.8362068965517241, 2.2327586206896552, 76.25, 54.474137931034484, 31.586206896551722, 9.64655172413793, 11.310344827586206, 5.077586206896552, 9.741379310344827), ('Ricki Osterthun', 0.4316546762589928, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Lawson Duncan', 0.4883720930232558, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 90.0, 1.0, 0.0, 70.5, 54.5, 33.5, 7.0, 9.5, 4.5, 7.5), ('Gilad Bloom', 0.4325581395348837, 105.7741935483871, 2.3870967741935485, 1.903225806451613, 73.87096774193549, 49.38709677419355, 34.774193548387096, 13.096774193548388, 11.483870967741936, 4.258064516129032, 6.129032258064516, 110.37777777777778, 2.2666666666666666, 3.0, 80.42222222222222, 49.24444444444445, 30.488888888888887, 14.333333333333334, 11.977777777777778, 5.155555555555556, 9.577777777777778), ('Marc Flur', 0.37623762376237624, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Darren Cahill', 0.5252918287937743, 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51.333333333333336, 37.44444444444444, 14.11111111111111, 11.777777777777779, 5.555555555555555, 7.333333333333333, 78.25, 2.25, 2.875, 66.75, 45.25, 27.625, 8.75, 9.9375, 4.3125, 8.125), ('Andrei Chesnokov', 0.5723684210526315, 99.64473684210526, 2.7756410256410255, 2.25, 75.06410256410257, 44.25, 30.141025641025642, 16.384615384615383, 11.448717948717949, 4.160256410256411, 6.711538461538462, 99.5578231292517, 2.9473684210526314, 3.2171052631578947, 82.1907894736842, 47.171052631578945, 29.24342105263158, 15.44078947368421, 12.006578947368421, 5.434210526315789, 10.18421052631579), ('Joao Cunha Silva', 0.34782608695652173, 118.38888888888889, 3.1578947368421053, 2.0, 84.73684210526316, 53.0, 37.05263157894737, 17.05263157894737, 13.210526315789474, 4.105263157894737, 6.473684210526316, 109.64444444444445, 2.122448979591837, 2.673469387755102, 81.3265306122449, 50.204081632653065, 30.857142857142858, 13.448979591836734, 11.612244897959183, 6.183673469387755, 10.693877551020408), ('Bud Schultz', 0.46534653465346537, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan), ('Franco Davin', 0.4967741935483871, 96.55, 1.1967213114754098, 1.5081967213114753, 71.18032786885246, 50.950819672131146, 33.19672131147541, 10.442622950819672, 11.0, 3.7704918032786887, 6.426229508196721, 101.17721518987342, 1.3209876543209877, 2.197530864197531, 78.80246913580247, 54.18518518518518, 31.395061728395063, 10.530864197530864, 11.691358024691358, 5.345679012345679, 10.333333333333334), ('Claudio Pistolesi', 0.4095744680851064, 111.0, 2.2, 1.35, 76.9, 54.6, 36.3, 12.95, 11.75, 5.05, 7.15, 112.48571428571428, 2.1714285714285713, 2.142857142857143, 80.48571428571428, 52.17142857142857, 31.17142857142857, 12.714285714285714, 12.17142857142857, 5.085714285714285, 9.885714285714286), ('Ronald Agenor', 0.4690082644628099, 101.32835820895522, 3.3043478260869565, 1.8115942028985508, 77.76811594202898, 49.666666666666664, 35.08695652173913, 15.0, 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4.111111111111111, 7.564814814814815), ('Horst Skoff', 0.5288683602771362, 103.6144578313253, 3.4819277108433737, 2.1686746987951806, 68.75903614457832, 39.36144578313253, 28.626506024096386, 16.3855421686747, 10.939759036144578, 3.4819277108433737, 5.301204819277109, 93.02105263157895, 2.3608247422680413, 3.082474226804124, 70.97938144329896, 42.371134020618555, 25.670103092783506, 12.484536082474227, 10.329896907216495, 4.639175257731959, 8.876288659793815), ('Alex Antonitsch', 0.4262948207171315, 94.34782608695652, 4.918367346938775, 2.4693877551020407, 75.85714285714286, 41.3469387755102, 31.163265306122447, 19.46938775510204, 11.571428571428571, 3.836734693877551, 5.285714285714286, 89.26865671641791, 4.041666666666667, 3.7777777777777777, 77.72222222222223, 42.861111111111114, 28.5, 15.25, 11.25, 5.013888888888889, 9.027777777777779), ('Udo Riglewski', 0.3524590163934426, 103.9090909090909, 4.454545454545454, 1.7272727272727273, 74.36363636363636, 44.45454545454545, 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14.321428571428571, 11.178571428571429, 4.5, 8.285714285714286), ('Milan Srejber', 0.4980544747081712, 129.0, 12.0, 5.0, 92.0, 61.0, 49.0, 20.0, 16.0, 1.0, 1.0, 59.333333333333336, 5.333333333333333, 5.0, 59.666666666666664, 33.666666666666664, 22.666666666666668, 10.666666666666666, 9.0, 4.333333333333333, 7.666666666666667), ('Javier Frana', 0.5092592592592593, 92.47272727272727, 6.225, 1.425, 76.94166666666666, 48.38333333333333, 36.975, 16.141666666666666, 12.125, 3.475, 4.766666666666667, 102.45652173913044, 6.319587628865979, 2.154639175257732, 88.65979381443299, 53.381443298969074, 36.88659793814433, 16.24742268041237, 13.29896907216495, 5.206185567010309, 9.041237113402062), ('Guillermo Perez Roldan', 0.6375661375661376, 97.95652173913044, 1.463768115942029, 1.0144927536231885, 69.1159420289855, 48.34782608695652, 32.72463768115942, 11.347826086956522, 10.710144927536232, 3.2753623188405796, 5.246376811594203, 99.8, 1.18, 1.6, 75.5, 49.82, 28.74, 11.6, 10.8, 5.64, 10.16), ('Carl Uwe Steeb', 0.4988235294117647, 99.25, 3.0306122448979593, 1.3265306122448979, 77.40816326530613, 46.66326530612245, 33.10204081632653, 17.224489795918366, 12.0, 3.9693877551020407, 5.887755102040816, 93.07563025210084, 2.564516129032258, 2.064516129032258, 75.99193548387096, 44.435483870967744, 28.06451612903226, 14.637096774193548, 11.298387096774194, 4.330645161290323, 8.28225806451613), ('Roberto Azar', 0.35251798561151076, 120.55555555555556, 2.4444444444444446, 2.888888888888889, 87.38888888888889, 63.888888888888886, 43.27777777777778, 11.555555555555555, 13.277777777777779, 5.111111111111111, 7.888888888888889, 97.30434782608695, 1.5833333333333333, 3.2291666666666665, 71.5, 47.6875, 27.833333333333332, 9.958333333333334, 10.791666666666666, 4.875, 9.416666666666666), ('Gary Muller', 0.4187192118226601, 96.14285714285714, 11.142857142857142, 3.4642857142857144, 80.82142857142857, 47.285714285714285, 37.75, 17.285714285714285, 12.642857142857142, 3.2857142857142856, 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4.9169230769230765, 2.163076923076923, 74.20153846153846, 47.096923076923076, 35.401538461538465, 15.4, 12.006153846153847, 3.1107692307692307, 4.463076923076923, 104.96335078534031, 4.316582914572864, 3.4321608040201004, 87.73366834170854, 52.8643216080402, 35.11055276381909, 16.311557788944725, 13.140703517587939, 5.201005025125628, 9.150753768844222), ('Javier Sanchez', 0.4947049924357035, 96.9656652360515, 2.7563025210084033, 2.096638655462185, 76.21008403361344, 51.07142857142857, 36.18487394957983, 13.474789915966387, 11.815126050420169, 3.8151260504201683, 5.7478991596638656, 94.88702928870293, 2.264, 2.82, 78.72, 50.652, 31.944, 12.392, 11.768, 4.804, 9.04), ('Tomas Carbonell', 0.46522781774580335, 97.96323529411765, 2.557142857142857, 3.342857142857143, 77.34285714285714, 54.8, 37.457142857142856, 10.885714285714286, 12.1, 4.128571428571429, 6.892857142857143, 90.86060606060606, 2.757396449704142, 4.3609467455621305, 77.71005917159763, 49.828402366863905, 30.39644970414201, 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8.964285714285714), ('Paul Haarhuis', 0.5095057034220533, 97.0, 5.109589041095891, 2.0639269406392695, 78.40182648401826, 47.49771689497717, 35.1324200913242, 16.926940639269407, 12.278538812785389, 3.684931506849315, 5.511415525114155, 96.705, 3.851674641148325, 2.444976076555024, 80.41148325358851, 47.75598086124402, 31.181818181818183, 15.038277511961722, 11.894736842105264, 5.023923444976076, 8.880382775119617), ('Guillaume Raoux', 0.4430693069306931, 93.32716049382717, 5.779761904761905, 2.625, 76.41071428571429, 41.25595238095238, 31.916666666666668, 19.898809523809526, 11.821428571428571, 3.363095238095238, 4.779761904761905, 97.44615384615385, 4.12621359223301, 3.3640776699029127, 81.6747572815534, 43.00485436893204, 29.699029126213592, 18.40776699029126, 12.233009708737864, 4.509708737864078, 8.160194174757281), ('Stefano Pescosolido', 0.40671641791044777, 103.48979591836735, 5.01980198019802, 3.5742574257425743, 80.20792079207921, 41.801980198019805, 31.85148514851485, 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17.11111111111111, 12.733333333333333, 5.177777777777778, 9.0), ('Cristiano Caratti', 0.38095238095238093, 102.828125, 2.546875, 3.0625, 81.96875, 52.1875, 36.0625, 16.484375, 12.4375, 4.125, 6.3125, 93.22857142857143, 1.7798165137614679, 3.458715596330275, 78.74311926605505, 50.11926605504587, 30.660550458715598, 12.321100917431192, 11.79816513761468, 4.944954128440367, 9.587155963302752), ('Mark Petchey', 0.3211009174311927, 92.45161290322581, 5.0625, 5.0, 81.875, 47.1875, 35.46875, 18.5, 12.40625, 4.15625, 5.8125, 91.18867924528301, 4.517857142857143, 5.196428571428571, 80.28571428571429, 46.464285714285715, 30.5, 14.375, 11.839285714285714, 4.910714285714286, 9.053571428571429), ('Henrik Holm', 0.463768115942029, 103.23809523809524, 9.395604395604396, 6.637362637362638, 85.38461538461539, 44.51648351648352, 36.13186813186813, 20.054945054945055, 12.934065934065934, 3.9450549450549453, 5.791208791208791, 104.55208333333333, 7.912621359223301, 7.766990291262136, 86.41747572815534, 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('Justin Gimelstob', 0.3835125448028674, 100.84905660377359, 7.5607476635514015, 3.897196261682243, 78.91588785046729, 46.598130841121495, 35.66355140186916, 16.80373831775701, 12.345794392523365, 3.5794392523364484, 5.336448598130841, 104.37647058823529, 6.111111111111111, 4.678362573099415, 80.90643274853801, 45.8421052631579, 32.01169590643275, 15.210526315789474, 12.169590643274853, 4.473684210526316, 8.128654970760234), ('Nicolas Escude', 0.5657894736842105, 102.8993710691824, 7.289308176100629, 3.1257861635220126, 79.49685534591195, 45.861635220125784, 34.9937106918239, 18.440251572327043, 12.666666666666666, 3.647798742138365, 5.408805031446541, 96.869918699187, 5.666666666666667, 3.902439024390244, 80.39837398373983, 43.886178861788615, 29.829268292682926, 16.32520325203252, 11.975609756097562, 4.780487804878049, 8.78048780487805), ('Galo Blanco', 0.4107744107744108, 99.1344537815126, 5.140495867768595, 2.628099173553719, 77.25619834710744, 43.17355371900826, 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('Ivan Ljubicic', 0.5906593406593407, 107.10552763819095, 12.628140703517587, 2.1758793969849246, 78.08542713567839, 46.42211055276382, 37.34422110552764, 17.668341708542712, 12.670854271356784, 2.9321608040201004, 3.9824120603015074, 114.94285714285714, 11.007142857142858, 2.9178571428571427, 85.86428571428571, 48.97857142857143, 35.957142857142856, 17.275, 13.15, 4.378571428571429, 7.492857142857143), ('Attila Savolt', 0.4158415841584158, 127.13793103448276, 5.933333333333334, 4.2, 84.73333333333333, 46.4, 35.3, 19.2, 13.433333333333334, 4.9, 7.666666666666667, 96.63829787234043, 4.191489361702128, 3.851063829787234, 71.17021276595744, 39.659574468085104, 26.80851063829787, 13.872340425531915, 10.914893617021276, 4.361702127659575, 8.085106382978724), ('Dominik Hrbaty', 0.5316642120765832, 93.08868501529052, 5.795731707317073, 3.7408536585365852, 76.22256097560975, 43.53353658536585, 32.646341463414636, 17.570121951219512, 12.033536585365853, 3.957317073170732, 5.826219512195122, 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4.738095238095238, 8.75), ('Marat Safin', 0.6118326118326118, 107.06958762886597, 9.421994884910486, 2.2404092071611252, 83.22762148337596, 46.148337595907925, 35.923273657289, 20.624040920716112, 13.327365728900256, 3.557544757033248, 5.2378516624040925, 111.625, 8.08298755186722, 2.759336099585062, 86.06224066390041, 47.365145228215766, 33.77593360995851, 18.24896265560166, 13.120331950207468, 4.589211618257261, 8.04149377593361), ('Alex Calatrava', 0.38764044943820225, 99.38805970149254, 7.134328358208955, 1.671641791044776, 75.19402985074628, 45.76119402985075, 34.73134328358209, 15.746268656716419, 11.985074626865671, 3.8955223880597014, 5.522388059701493, 103.29126213592232, 5.563106796116505, 2.4271844660194173, 79.52427184466019, 46.067961165048544, 31.145631067961165, 15.349514563106796, 12.233009708737864, 4.815533980582524, 8.660194174757281), ('Arnaud Di Pasquale', 0.41317365269461076, 94.93442622950819, 2.6825396825396823, 3.5238095238095237, 78.25396825396825, 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8.785714285714286), ('Peter Wessels', 0.42857142857142855, 89.04761904761905, 10.023809523809524, 2.880952380952381, 75.5, 47.45238095238095, 37.76190476190476, 16.047619047619047, 12.476190476190476, 2.4285714285714284, 3.4523809523809526, 91.89655172413794, 8.293103448275861, 3.5517241379310347, 83.27586206896552, 51.810344827586206, 36.60344827586207, 14.327586206896552, 12.758620689655173, 4.724137931034483, 8.10344827586207), ('Nicolas Massu', 0.5211267605633803, 117.91943127962085, 4.6635071090047395, 2.559241706161137, 74.96208530805687, 44.72985781990521, 32.682464454976305, 16.696682464454977, 11.682464454976303, 3.8578199052132702, 5.6635071090047395, 116.14285714285714, 4.15668202764977, 3.3640552995391704, 79.87096774193549, 46.17972350230415, 30.258064516129032, 15.276497695852534, 11.829493087557603, 5.373271889400922, 9.341013824884792), ('Martin Verkerk', 0.46153846153846156, 107.23636363636363, 14.357142857142858, 4.571428571428571, 81.51785714285714, 48.875, 39.017857142857146, 18.017857142857142, 13.446428571428571, 3.107142857142857, 4.464285714285714, 100.55384615384615, 10.784615384615385, 5.1692307692307695, 83.53846153846153, 49.353846153846156, 35.83076923076923, 15.76923076923077, 12.569230769230769, 4.553846153846154, 7.553846153846154), ('Harel Levy', 0.3888888888888889, 87.11627906976744, 4.651162790697675, 1.7906976744186047, 73.04651162790698, 43.44186046511628, 33.18604651162791, 16.13953488372093, 11.69767441860465, 3.8372093023255816, 5.3023255813953485, 86.07228915662651, 3.5783132530120483, 2.0240963855421685, 73.73493975903614, 42.30120481927711, 27.397590361445783, 13.843373493975903, 11.060240963855422, 4.819277108433735, 8.795180722891565), ('Paul Goldstein', 0.425, 92.97647058823529, 2.3176470588235296, 2.235294117647059, 76.4, 49.32941176470588, 34.28235294117647, 14.447058823529412, 11.611764705882353, 4.235294117647059, 6.305882352941176, 98.41964285714286, 2.3859649122807016, 2.973684210526316, 80.63157894736842, 48.3859649122807, 29.75438596491228, 14.535087719298245, 11.763157894736842, 5.5964912280701755, 10.149122807017545), ('Taylor Dent', 0.5187713310580204, 88.71428571428571, 8.448979591836734, 5.557823129251701, 74.72108843537416, 42.843537414965986, 34.244897959183675, 16.94557823129252, 11.97278911564626, 3.204081632653061, 4.619047619047619, 94.6962962962963, 6.522058823529412, 6.073529411764706, 80.66176470588235, 45.85294117647059, 32.279411764705884, 15.602941176470589, 12.419117647058824, 4.426470588235294, 8.073529411764707), ('Roger Federer', 0.8180636777128005, 96.89595872742906, 7.915481171548117, 1.6619246861924686, 74.14309623430962, 46.44267782426778, 36.69372384937238, 16.471966527196653, 12.511297071129707, 2.5447698744769873, 3.5238493723849373, 124.37164750957854, 7.4562737642585555, 2.8631178707224336, 95.56653992395437, 57.657794676806084, 40.961977186311785, 18.231939163498097, 14.545454545454545, 4.904942965779468, 8.463878326996198), ('Raemon Sluiter', 0.4072398190045249, 92.86904761904762, 8.178571428571429, 3.0714285714285716, 74.33333333333333, 44.083333333333336, 34.523809523809526, 16.61904761904762, 12.119047619047619, 3.4523809523809526, 4.892857142857143, 102.1, 6.466666666666667, 3.7, 82.95, 48.34166666666667, 33.516666666666666, 16.658333333333335, 12.791666666666666, 4.558333333333334, 8.083333333333334), ('Fernando Gonzalez', 0.6464471403812825, 102.4327485380117, 7.611111111111111, 3.6140350877192984, 78.86842105263158, 48.578947368421055, 37.4766081871345, 16.616959064327485, 12.789473684210526, 3.2134502923976607, 4.657894736842105, 109.57068062827226, 5.895287958115183, 4.952879581151833, 86.21989528795811, 51.539267015706805, 35.78010471204188, 16.0, 13.047120418848168, 5.020942408376963, 8.565445026178011), ('Danai Udomchoke', 0.4580152671755725, 106.94444444444444, 4.333333333333333, 2.888888888888889, 84.0, 52.166666666666664, 38.27777777777778, 16.27777777777778, 12.944444444444445, 3.8333333333333335, 5.944444444444445, 96.82692307692308, 3.1153846153846154, 3.8846153846153846, 80.32692307692308, 49.19230769230769, 31.423076923076923, 13.884615384615385, 11.923076923076923, 5.230769230769231, 9.576923076923077), ('Juan Carlos Ferrero', 0.6464237516869096, 109.57777777777778, 4.326666666666667, 2.1866666666666665, 78.60444444444444, 50.44, 36.34, 16.184444444444445, 12.517777777777777, 3.8555555555555556, 5.637777777777778, 110.62204724409449, 3.5551181102362204, 2.393700787401575, 81.33858267716535, 51.82283464566929, 33.17716535433071, 14.374015748031496, 12.421259842519685, 4.913385826771654, 8.803149606299213), ('Andreas Vinciguerra', 0.45789473684210524, 91.6867469879518, 4.927710843373494, 2.4457831325301207, 75.66265060240964, 45.78313253012048, 34.036144578313255, 16.156626506024097, 11.674698795180722, 4.036144578313253, 5.662650602409639, 92.92222222222222, 4.033333333333333, 3.111111111111111, 76.87777777777778, 44.41111111111111, 28.944444444444443, 14.833333333333334, 11.277777777777779, 5.622222222222222, 9.344444444444445), ('Tommy Robredo', 0.5966480446927375, 107.75638506876228, 4.21414913957935, 2.225621414913958, 78.49713193116635, 51.30592734225621, 37.27342256214149, 15.049713193116634, 12.489483747609942, 3.676864244741874, 5.489483747609943, 102.46567164179105, 2.956772334293948, 2.5072046109510087, 76.76657060518733, 49.05763688760807, 31.51585014409222, 12.902017291066283, 11.789625360230547, 4.688760806916426, 8.570605187319885), ('Radek Stepanek', 0.5586107091172214, 103.1510989010989, 6.929539295392954, 2.6395663956639566, 75.26287262872629, 47.6260162601626, 36.552845528455286, 15.311653116531165, 12.154471544715447, 3.1815718157181574, 4.436314363143631, 111.80782918149467, 5.380622837370242, 3.6193771626297577, 82.26297577854672, 49.88581314878893, 33.87543252595156, 14.785467128027681, 12.53287197231834, 4.643598615916955, 8.415224913494809), ('Tomas Zib', 0.30714285714285716, 95.6829268292683, 4.317073170731708, 1.9268292682926829, 73.95121951219512, 43.951219512195124, 32.146341463414636, 16.317073170731707, 11.658536585365853, 3.5853658536585367, 5.634146341463414, 95.5, 3.282608695652174, 2.010869565217391, 75.42391304347827, 44.608695652173914, 28.67391304347826, 14.445652173913043, 11.326086956521738, 4.739130434782608, 8.597826086956522), ('Kristian Pless', 0.3900709219858156, 85.72093023255815, 6.953488372093023, 3.0, 74.13953488372093, 46.72093023255814, 35.53488372093023, 15.69767441860465, 12.255813953488373, 3.302325581395349, 4.674418604651163, 92.55555555555556, 5.7407407407407405, 3.888888888888889, 82.32098765432099, 49.32098765432099, 32.98765432098765, 15.814814814814815, 12.530864197530864, 4.802469135802469, 8.432098765432098), ('Christophe Rochus', 0.35014836795252224, 95.12389380530973, 2.47787610619469, 2.752212389380531, 77.33628318584071, 48.15929203539823, 33.50442477876106, 15.283185840707965, 11.769911504424778, 4.539823008849558, 6.849557522123894, 86.3252427184466, 1.4951456310679612, 3.004854368932039, 72.86407766990291, 43.64563106796116, 26.271844660194176, 12.674757281553399, 11.014563106796116, 4.41747572815534, 9.053398058252426), ('Gaston Gaudio', 0.5811965811965812, 103.45490196078431, 2.925490196078431, 2.607843137254902, 74.32941176470588, 46.19607843137255, 32.85490196078431, 15.352941176470589, 11.741176470588234, 3.7490196078431373, 5.8392156862745095, 112.86772486772487, 2.4814814814814814, 3.751322751322751, 84.12698412698413, 52.074074074074076, 32.111111111111114, 14.201058201058201, 12.46031746031746, 5.64021164021164, 10.481481481481481), ('Agustin Calleri', 0.5263157894736842, 97.435, 7.26, 2.925, 70.575, 38.995, 30.605, 17.32, 11.435, 3.085, 4.575, 108.69444444444444, 6.613259668508287, 3.972375690607735, 84.33701657458563, 46.74585635359116, 33.02209944751381, 16.994475138121548, 12.767955801104973, 4.801104972375691, 8.546961325966851), ('James Blake', 0.5878594249201278, 89.657060518732, 6.481268011527377, 2.5187319884726227, 75.63400576368876, 44.28530259365994, 33.60806916426513, 17.21037463976945, 12.005763688760807, 3.363112391930836, 4.9193083573487035, 104.46694214876032, 5.032921810699588, 3.168724279835391, 87.40329218106996, 49.88065843621399, 34.13168724279836, 17.473251028806583, 13.148148148148149, 4.94238683127572, 8.872427983539094), ('Bjorn Phau', 0.3669724770642202, 93.5375, 4.325, 2.8875, 70.8625, 37.4375, 27.625, 18.625, 11.15, 3.5625, 5.45, 96.32608695652173, 3.3115942028985508, 3.927536231884058, 75.6086956521739, 39.8768115942029, 25.86231884057971, 16.347826086956523, 11.456521739130435, 4.644927536231884, 8.855072463768115), ('Jean Rene Lisnard', 0.37857142857142856, 124.26829268292683, 2.5365853658536586, 4.2439024390243905, 90.1951219512195, 49.31707317073171, 34.58536585365854, 20.390243902439025, 13.463414634146341, 5.170731707317073, 8.341463414634147, 101.87179487179488, 2.0, 4.5256410256410255, 78.61538461538461, 40.07692307692308, 25.256410256410255, 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Gil', 0.46853146853146854, 116.0, 2.5918367346938775, 1.8571428571428572, 79.57142857142857, 50.734693877551024, 35.69387755102041, 15.326530612244898, 12.244897959183673, 4.244897959183674, 6.3061224489795915, 100.33333333333333, 2.257575757575758, 2.227272727272727, 74.78787878787878, 47.96969696969697, 28.984848484848484, 12.636363636363637, 11.333333333333334, 4.878787878787879, 9.136363636363637), ('Pablo Cuevas', 0.5230769230769231, 105.67647058823529, 6.439814814814815, 2.449074074074074, 74.89351851851852, 45.98148148148148, 35.16203703703704, 16.15740740740741, 12.101851851851851, 3.1296296296296298, 4.638888888888889, 108.41624365482234, 5.246445497630332, 3.132701421800948, 80.77725118483413, 48.4218009478673, 32.426540284360186, 15.497630331753555, 12.388625592417062, 4.748815165876778, 8.398104265402843), ('Andy Murray', 0.7616974972796517, 114.8755905511811, 7.115963855421687, 2.3870481927710845, 76.980421686747, 45.2394578313253, 34.71686746987952, 17.302710843373493, 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4.722222222222222, 8.819444444444445), ('Sam Querrey', 0.5455840455840456, 93.91988950276243, 13.951742627345844, 3.0777479892761392, 77.09651474530831, 46.25469168900804, 38.34048257372654, 16.718498659517426, 12.766756032171582, 2.753351206434316, 3.7184986595174263, 104.9090909090909, 11.524271844660195, 3.8770226537216828, 85.89320388349515, 49.9126213592233, 37.226537216828476, 16.631067961165048, 13.508090614886731, 3.9935275080906147, 7.174757281553398), ('Evgeny Korolev', 0.43023255813953487, 100.31506849315069, 5.3561643835616435, 3.232876712328767, 77.21917808219177, 45.75342465753425, 33.945205479452056, 16.273972602739725, 12.027397260273972, 3.9726027397260273, 6.0, 96.30434782608695, 4.5, 3.7934782608695654, 75.77173913043478, 43.53260869565217, 29.608695652173914, 14.217391304347826, 11.467391304347826, 4.641304347826087, 8.467391304347826), ('Thiemo De Bakker', 0.4268292682926829, 107.89090909090909, 9.345454545454546, 1.5636363636363637, 79.61818181818182, 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Lennard Struff', 0.46348314606741575, 100.58024691358025, 10.364197530864198, 3.1481481481481484, 78.18518518518519, 43.925925925925924, 35.129629629629626, 18.65432098765432, 12.530864197530864, 3.1728395061728394, 4.5, 105.29891304347827, 7.851851851851852, 4.169312169312169, 83.23280423280423, 46.32275132275132, 33.804232804232804, 16.74074074074074, 12.777777777777779, 4.343915343915344, 7.788359788359788), ('Peter Gojowczyk', 0.4585987261146497, 97.64788732394366, 8.295774647887324, 3.507042253521127, 80.56338028169014, 44.690140845070424, 35.45070422535211, 19.43661971830986, 12.816901408450704, 3.2816901408450705, 4.788732394366197, 97.74698795180723, 6.226190476190476, 4.273809523809524, 80.05952380952381, 43.142857142857146, 30.261904761904763, 15.952380952380953, 12.071428571428571, 4.928571428571429, 8.845238095238095), ('Karen Khachanov', 0.5584415584415584, 112.98181818181818, 8.698795180722891, 2.5903614457831323, 81.99397590361446, 51.18674698795181, 39.265060240963855, 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8.16891891891892), ('Taro Daniel', 0.375, 115.71153846153847, 3.339622641509434, 2.7358490566037736, 78.86792452830188, 49.75471698113208, 35.43396226415094, 16.11320754716981, 12.377358490566039, 4.056603773584905, 6.037735849056604, 115.26190476190476, 3.5930232558139537, 3.13953488372093, 82.81395348837209, 51.872093023255815, 32.69767441860465, 14.116279069767442, 12.406976744186046, 5.348837209302325, 9.80232558139535), ('Borna Coric', 0.5382165605095541, 118.8258064516129, 6.5636363636363635, 2.066666666666667, 79.04848484848485, 49.31515151515151, 37.4, 17.2, 12.83030303030303, 3.5393939393939395, 4.951515151515151, 115.176, 5.285714285714286, 2.45, 79.77857142857142, 49.628571428571426, 33.628571428571426, 14.207142857142857, 12.271428571428572, 4.235714285714286, 7.685714285714286), ('Roberto Carballes Baena', 0.44366197183098594, 115.85714285714286, 2.888888888888889, 3.0, 76.25396825396825, 48.95238095238095, 34.682539682539684, 15.158730158730158, 11.968253968253968, 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14.953703703703704, 12.407407407407407, 4.638888888888889, 8.222222222222221), ('Hyeon Chung', 0.5490196078431373, 125.25, 4.379746835443038, 2.9240506329113924, 75.68354430379746, 47.81012658227848, 36.050632911392405, 15.240506329113924, 12.265822784810126, 3.5063291139240507, 5.037974683544304, 101.38983050847457, 2.791044776119403, 2.8208955223880596, 74.8955223880597, 46.04477611940298, 29.65671641791045, 13.253731343283581, 11.656716417910447, 3.925373134328358, 7.970149253731344), ('Nikoloz Basilashvili', 0.48507462686567165, 108.17073170731707, 4.650793650793651, 4.015873015873016, 83.33333333333333, 49.70634920634921, 35.79365079365079, 17.91269841269841, 12.801587301587302, 3.9365079365079363, 6.150793650793651, 101.87022900763358, 3.5220588235294117, 4.897058823529412, 81.80882352941177, 48.654411764705884, 30.830882352941178, 15.257352941176471, 12.183823529411764, 5.227941176470588, 9.529411764705882), ('Nicolas Jarry', 0.4732142857142857, 109.40816326530613, 8.9, 2.5, 81.18, 51.38, 39.54, 16.8, 13.18, 3.22, 4.64, 115.10526315789474, 9.568965517241379, 3.3620689655172415, 91.32758620689656, 57.58620689655172, 39.6551724137931, 16.603448275862068, 13.758620689655173, 4.931034482758621, 8.275862068965518), ('Jared Donaldson', 0.4247787610619469, 103.9090909090909, 7.212765957446808, 3.872340425531915, 76.91489361702128, 40.319148936170215, 31.97872340425532, 19.893617021276597, 12.361702127659575, 2.9574468085106385, 4.531914893617022, 109.37288135593221, 5.2615384615384615, 4.030769230769231, 81.55384615384615, 42.353846153846156, 29.6, 19.153846153846153, 12.507692307692308, 4.3538461538461535, 8.061538461538461), ('Yoshihito Nishioka', 0.4437869822485207, 109.43243243243244, 2.0, 1.4933333333333334, 78.89333333333333, 52.54666666666667, 36.28, 15.733333333333333, 12.64, 4.173333333333333, 6.16, 102.98876404494382, 1.6195652173913044, 1.9673913043478262, 77.04347826086956, 49.41304347826087, 29.141304347826086, 13.043478260869565, 11.51086956521739, 5.173913043478261, 9.75), ('Taylor Fritz', 0.5177865612648221, 104.11627906976744, 10.076923076923077, 2.5692307692307694, 80.4, 47.676923076923075, 37.06923076923077, 18.0, 12.792307692307693, 3.4846153846153847, 4.876923076923077, 106.60504201680672, 8.401639344262295, 2.721311475409836, 83.68032786885246, 48.450819672131146, 34.9344262295082, 16.311475409836067, 12.942622950819672, 4.147540983606557, 7.5), ('Jaume Munar', 0.39090909090909093, 119.14285714285714, 3.7906976744186047, 2.441860465116279, 78.02325581395348, 50.30232558139535, 35.95348837209303, 14.604651162790697, 11.953488372093023, 4.116279069767442, 6.232558139534884, 126.51515151515152, 3.671641791044776, 2.91044776119403, 87.38805970149254, 56.07462686567164, 36.74626865671642, 14.044776119402986, 12.985074626865671, 5.492537313432836, 9.656716417910447), ('Frances Tiafoe', 0.4657534246575342, 111.87, 7.6138613861386135, 2.227722772277228, 83.06930693069307, 52.24752475247525, 39.24752475247525, 17.22772277227723, 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13.23076923076923, 4.948717948717949, 8.64102564102564), ('Ilya Ivashka', 0.5277777777777778, 115.87037037037037, 7.62962962962963, 2.8703703703703702, 79.62962962962963, 49.51851851851852, 37.666666666666664, 16.38888888888889, 12.685185185185185, 3.4074074074074074, 5.018518518518518, 122.20833333333333, 6.0212765957446805, 3.8297872340425534, 84.68085106382979, 50.234042553191486, 34.57446808510638, 15.382978723404255, 12.97872340425532, 4.8936170212765955, 8.936170212765957), ('Casper Ruud', 0.6236559139784946, 104.03539823008849, 4.964601769911504, 1.9823008849557522, 71.87610619469027, 47.0353982300885, 35.29203539823009, 14.575221238938052, 11.902654867256636, 2.6991150442477876, 3.8849557522123894, 105.57352941176471, 3.7681159420289854, 2.608695652173913, 77.01449275362319, 50.05797101449275, 32.68115942028985, 12.710144927536232, 11.72463768115942, 4.492753623188406, 8.043478260869565), ('Lloyd Harris', 0.5299145299145299, 106.75, 12.116666666666667, 2.3833333333333333, 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2.961904761904762, 81.1047619047619, 52.31428571428572, 34.41904761904762, 13.352380952380953, 12.371428571428572, 4.180952380952381, 7.9523809523809526), ('Tommy Paul', 0.5, 106.09803921568627, 4.901960784313726, 1.9019607843137254, 76.37254901960785, 46.88235294117647, 35.09803921568628, 16.568627450980394, 12.372549019607844, 3.3529411764705883, 4.980392156862745, 113.63265306122449, 4.02, 2.74, 84.2, 50.32, 32.4, 16.28, 12.76, 4.84, 8.92), ('Daniil Medvedev', 0.69375, 100.39170506912443, 8.907407407407407, 3.0416666666666665, 71.67592592592592, 43.583333333333336, 34.63425925925926, 15.574074074074074, 11.981481481481481, 2.6435185185185186, 3.837962962962963, 111.6421052631579, 6.536082474226804, 3.9690721649484537, 84.51546391752578, 49.69072164948454, 33.402061855670105, 16.11340206185567, 12.762886597938145, 4.989690721649485, 8.938144329896907), ('Denis Shapovalov', 0.5596707818930041, 114.65671641791045, 9.253731343283581, 5.104477611940299, 80.79850746268657, 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0.5478723404255319, 104.76699029126213, 5.233009708737864, 3.0679611650485437, 73.66990291262135, 48.980582524271846, 36.067961165048544, 12.864077669902912, 11.660194174757281, 3.766990291262136, 5.339805825242719, 111.75294117647059, 4.211764705882353, 3.6470588235294117, 82.92941176470588, 53.07058823529412, 34.71764705882353, 13.823529411764707, 12.776470588235295, 4.776470588235294, 8.658823529411764), ('Tennys Sandgren', 0.3949579831932773, 117.27659574468085, 10.553191489361701, 2.382978723404255, 85.55319148936171, 53.08510638297872, 41.276595744680854, 17.93617021276596, 14.0, 3.25531914893617, 4.808510638297872, 114.52112676056338, 7.535211267605634, 2.9014084507042255, 84.54929577464789, 50.95774647887324, 35.59154929577465, 14.915492957746478, 12.67605633802817, 5.154929577464789, 8.690140845070422), ('Stefanos Tsitsipas', 0.6654929577464789, 107.85245901639344, 6.681318681318682, 1.9230769230769231, 75.34065934065934, 47.04945054945055, 37.01648351648352, 16.46153846153846, 12.587912087912088, 2.620879120879121, 3.7362637362637363, 125.31914893617021, 7.638297872340425, 2.9893617021276597, 89.8936170212766, 54.93617021276596, 39.265957446808514, 17.06382978723404, 13.925531914893616, 4.446808510638298, 7.648936170212766), ('Matteo Berrettini', 0.6363636363636364, 114.87387387387388, 10.621621621621621, 1.9189189189189189, 79.62162162162163, 50.21621621621622, 41.108108108108105, 16.594594594594593, 13.348214285714286, 2.90990990990991, 3.810810810810811, 116.890625, 7.650793650793651, 2.126984126984127, 84.57142857142857, 51.93650793650794, 37.095238095238095, 16.11111111111111, 13.158730158730158, 4.523809523809524, 7.492063492063492), ('Lorenzo Sonego', 0.4962962962962963, 120.77272727272727, 6.954545454545454, 1.606060606060606, 79.43939393939394, 51.303030303030305, 39.621212121212125, 15.772727272727273, 12.787878787878787, 3.0454545454545454, 4.363636363636363, 121.04477611940298, 5.432835820895522, 2.343283582089552, 82.77611940298507, 53.149253731343286, 36.492537313432834, 13.865671641791044, 12.597014925373134, 4.432835820895522, 7.611940298507463), ('Felix Auger Aliassime', 0.5738636363636364, 113.76, 9.82, 3.73, 75.42, 49.02, 38.67, 14.29, 12.37, 2.83, 4.03, 117.86486486486487, 7.027027027027027, 5.135135135135135, 78.91891891891892, 49.108108108108105, 33.932432432432435, 13.283783783783784, 12.283783783783784, 5.0, 8.635135135135135), ('Ugo Humbert', 0.5126050420168067, 111.13793103448276, 7.931034482758621, 2.689655172413793, 78.6896551724138, 49.46551724137931, 38.58620689655172, 16.275862068965516, 12.620689655172415, 3.3275862068965516, 4.603448275862069, 125.35714285714286, 7.607142857142857, 3.392857142857143, 90.82142857142857, 54.714285714285715, 39.214285714285715, 17.553571428571427, 14.071428571428571, 5.160714285714286, 8.464285714285714), ('Miomir Kecmanovic', 0.4700854700854701, 102.4074074074074, 4.981481481481482, 2.3333333333333335, 73.79629629629629, 44.24074074074074, 33.74074074074074, 16.796296296296298, 12.0, 3.1296296296296298, 4.703703703703703, 106.5, 3.459016393442623, 2.540983606557377, 81.49180327868852, 49.36065573770492, 31.950819672131146, 15.836065573770492, 11.950819672131148, 5.622950819672131, 9.37704918032787), ('Jannik Sinner', 0.6532258064516129, 106.1625, 4.564102564102564, 1.8205128205128205, 74.57692307692308, 45.0, 33.217948717948715, 16.53846153846154, 11.782051282051283, 4.076923076923077, 5.641025641025641, 118.5813953488372, 3.4651162790697674, 2.3488372093023258, 82.32558139534883, 49.25581395348837, 32.58139534883721, 15.976744186046512, 12.372093023255815, 4.767441860465116, 8.604651162790697)]

Now I can combine this all into its own datatframe

playersDataframe = pd.DataFrame.from_records(winRates, columns =['Player', 'Winrate','Winning Minutes', 'Winning Aces' , 'Winning Double Fault', 'Winning Serve Point', 'Winning First In', 'Winning First Point Won',\
                                                   'Winning Second Point Won', 'Winning Serve Games', 'Winning Break Points Saved', 'Winning Break Points Faced','Losing Minutes','Losing Aces' ,\
                                                   'Losing Double Fault', 'Losing Serve Point', 'Losing First In', 'Losing First Point Won',\
                                                   'Losing Second Point Won', 'Losing Serve Games', 'Losing Break Points Saved', 'Losing Break Points Faced'])
playersDataframe.dropna(how = 'any', inplace = True)
display(playersDataframe)

Now I can do a preliminarly matrix plot to see if anyting is correlated with winrate well

import seaborn as sns
predictors = []
sns.pairplot(playersDataframe)

<seaborn.axisgrid.PairGrid at 0x7f7280aa1150>

In this matrix plot, we would be looking for any variables that are correlated with winrate, but not correlated with each other so when we use them in the model, they do not explain the same variance.

However, looking at the diagonal of our matrix plot, we can see that most of our stats look normally distributed. This is a problem, because if we look at our scatter plots, none of our variables are correlated with winrate very well. This does not bode well for the model.

Nevertheless, I will attempt to continue with the model starting with a multiple regression model against all our statistics.

import statsmodels.api as sm
X = playersDataframe[['Winning Minutes', 'Winning Aces' , 'Winning Double Fault', 'Winning Serve Point', 'Winning First In', 'Winning First Point Won',\
                                                   'Winning Second Point Won', 'Winning Serve Games', 'Winning Break Points Saved', 'Winning Break Points Faced','Losing Minutes','Losing Aces' ,\
                                                   'Losing Double Fault', 'Losing Serve Point', 'Losing First In', 'Losing First Point Won',\
                                                   'Losing Second Point Won', 'Losing Serve Games', 'Losing Break Points Saved', 'Losing Break Points Faced']]
y = playersDataframe['Winrate']

X = sm.add_constant(X)
est = sm.OLS(y, X).fit()
est.summary()

/usr/local/lib/python3.7/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.
  import pandas.util.testing as tm
/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/tsatools.py:117: FutureWarning: In a future version of pandas all arguments of concat except for the argument 'objs' will be keyword-only
  x = pd.concat(x[::order], 1)

I'm going to use backward selection in an attempt to improve my model.

Backwards selection means I will try and prune high p-value predicotrs out of the model as the high p-value shows that it adds little to the model. However, the model looks doomed because of the low R-Squared value.

In spite of this, we will continue.

X = playersDataframe[['Winning Minutes','Winning Double Fault', 'Winning Serve Games', 'Winning Break Points Saved', 'Winning Break Points Faced','Losing Minutes',\
                                                   'Losing Double Fault', 'Losing Serve Point', 'Losing First Point Won',\
                                                   'Losing Second Point Won', 'Losing Serve Games', 'Losing Break Points Saved', 'Losing Break Points Faced']]
y = playersDataframe['Winrate']

X = sm.add_constant(X)
est = sm.OLS(y, X).fit()
est.summary()

/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/tsatools.py:117: FutureWarning: In a future version of pandas all arguments of concat except for the argument 'objs' will be keyword-only
  x = pd.concat(x[::order], 1)

Looking at the coefficents, they make sense. For example, winning minutes has a negative coefficient, as taking longer to win your games may mean you are a less dominant player in your games, thus having a lower winrate.

However, our R-Sqaured value is quite low, making this a bad model. This could be a result of tennis statistics being normally distributed, and looking at our matrix plot, it's hard to see correlations when all the points are bunched together.

We will continue selecting only the low p-value predcitors. With an alpha (significance level) of 0.05, there are only 2 predictors that satifsy this alpha.

X = playersDataframe[['Winning Minutes','Losing Minutes']]
y = playersDataframe['Winrate']

X = sm.add_constant(X)
est = sm.OLS(y, X).fit()
est.summary()

/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/tsatools.py:117: FutureWarning: In a future version of pandas all arguments of concat except for the argument 'objs' will be keyword-only
  x = pd.concat(x[::order], 1)

liminating all of the predictors with p-values higher than a signfiicance level of 0.05 leaves us with just two predictors. Funnily enough, this tanks our R-squared making our already terrible model worse.

Looking at the scatter plots, I was already skeptical that a linear model would work to predict winrates. I was somewhat optimistic though, playing the game of tennis myself because I thought stats could show what made good players good, and what stats I could focus on myself to improve my winrate.

The only results that make sense are that the faster you win your games, the better your winrate. Similarly, the longer it takes you to lose, the better your winrate.

Conclusion. Currently, with publicly available stats, I can't make a linear model to predict wins. It seems like the stats people are choosing to document don't really matter. Maybe with more sophsticated stats such as shot selection, or percentage in that professional currently use, I could get more meaningful results.

After all this doom and gloom, I wanted to satisfy my fan perspective, comparing the best of the best players against each other. I decided to compare Federer, Nadal, and Novak.

# Analyze a single player's winrate over time
# let's look at how novak's stats in wins have changed yearly
novak = combined.loc[(combined['winner_name'] ==  'Novak Djokovic')]
nadal = combined.loc[(combined['winner_name'] ==  'Rafael Nadal')]
federer = combined.loc[(combined['winner_name'] ==  'Roger Federer')]

# Initialize stats array
novakStatsOverTime = []
nadalStatsOverTime = []
federerStatsOverTime = []

for year in range(2000,2022):
    wins = novak.loc[novak['tourney_date'] < datetime.datetime(year,1,1)]
    winAvgs = wins.mean()
    novakStatsOverTime.append((year, winAvgs['minutes'], winAvgs['w_ace'], winAvgs['w_df'], winAvgs['w_svpt'] \
                      , winAvgs['w_1stIn'], winAvgs['w_1stWon'], winAvgs['w_2ndWon'], winAvgs['w_SvGms'], winAvgs['w_bpSaved'], winAvgs['w_bpFaced']))
    
    wins = nadal.loc[nadal['tourney_date'] < datetime.datetime(year,1,1)]
    winAvgs = wins.mean()
    nadalStatsOverTime.append((year, winAvgs['minutes'], winAvgs['w_ace'], winAvgs['w_df'], winAvgs['w_svpt'] \
                      , winAvgs['w_1stIn'], winAvgs['w_1stWon'], winAvgs['w_2ndWon'], winAvgs['w_SvGms'], winAvgs['w_bpSaved'], winAvgs['w_bpFaced']))

    wins = federer.loc[federer['tourney_date'] < datetime.datetime(year,1,1)]
    winAvgs = wins.mean()
    federerStatsOverTime.append((year, winAvgs['minutes'], winAvgs['w_ace'], winAvgs['w_df'], winAvgs['w_svpt'] \
                      , winAvgs['w_1stIn'], winAvgs['w_1stWon'], winAvgs['w_2ndWon'], winAvgs['w_SvGms'], winAvgs['w_bpSaved'], winAvgs['w_bpFaced']))
    


novakOverTime = pd.DataFrame.from_records(novakStatsOverTime, columns =['Year','Winning Minutes', 'Winning Aces' , 'Winning Double Fault', 'Winning Serve Point', 'Winning First In', 'Winning First Point Won',\
                                                   'Winning Second Point Won', 'Winning Serve Games', 'Winning Break Points Saved', 'Winning Break Points Faced'])
nadalOverTime = pd.DataFrame.from_records(nadalStatsOverTime, columns =['Year','Winning Minutes', 'Winning Aces' , 'Winning Double Fault', 'Winning Serve Point', 'Winning First In', 'Winning First Point Won',\
                                                   'Winning Second Point Won', 'Winning Serve Games', 'Winning Break Points Saved', 'Winning Break Points Faced'])
federerOverTime = pd.DataFrame.from_records(federerStatsOverTime, columns =['Year','Winning Minutes', 'Winning Aces' , 'Winning Double Fault', 'Winning Serve Point', 'Winning First In', 'Winning First Point Won',\
                                                   'Winning Second Point Won', 'Winning Serve Games', 'Winning Break Points Saved', 'Winning Break Points Faced'])

plt.plot(novakOverTime['Year'], novakOverTime["Winning Serve Point"], label = 'Novak')
plt.plot(nadalOverTime['Year'], nadalOverTime["Winning Serve Point"], label = 'Nadal')
plt.plot(federerOverTime['Year'], federerOverTime["Winning Serve Point"], label = 'Federer')
plt.xlabel("Years")
plt.ylabel("")
plt.title("Number of Minutes to Win over Time")
plt.legend()

/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:14: FutureWarning: DataFrame.mean and DataFrame.median with numeric_only=None will include datetime64 and datetime64tz columns in a future version.
  
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:18: FutureWarning: DataFrame.mean and DataFrame.median with numeric_only=None will include datetime64 and datetime64tz columns in a future version.
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:22: FutureWarning: DataFrame.mean and DataFrame.median with numeric_only=None will include datetime64 and datetime64tz columns in a future version.
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:22: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:18: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:14: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.

<matplotlib.legend.Legend at 0x7f72663cbdd0>

	tourney_id	tourney_name	surface	draw_size	tourney_level	tourney_date	match_num	winner_id	winner_seed	winner_entry	winner_name	winner_hand	winner_ht	winner_ioc	winner_age	loser_id	loser_seed	loser_entry	loser_name	loser_hand	loser_ht	loser_ioc	loser_age	score	best_of	round	minutes	w_ace	w_df	w_svpt	w_1stIn	w_1stWon	w_2ndWon	w_SvGms	w_bpSaved	w_bpFaced	l_ace	l_df	l_svpt	l_1stIn	l_1stWon	l_2ndWon	l_SvGms	l_bpSaved	l_bpFaced	winner_rank	winner_rank_points	loser_rank	loser_rank_points
0	1968-2029	Dublin	Grass	32	A	1968-07-08	270	202866	NaN	NaN	Doug Smith	U	NaN	AUS	NaN	110196	NaN	NaN	Peter Ledbetter	U	NaN	UNK	NaN	6-1 7-5	3	R32	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	1968-2029	Dublin	Grass	32	A	1968-07-08	271	126914	NaN	NaN	Louis Pretorius	R	NaN	RSA	NaN	209536	NaN	NaN	Maurice Pollock	U	NaN	IRL	NaN	6-1 6-1	3	R32	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2	1968-2029	Dublin	Grass	32	A	1968-07-08	272	209523	NaN	NaN	Cecil Pedlow	U	NaN	IRL	NaN	209535	NaN	NaN	John Mulvey	U	NaN	IRL	NaN	6-2 6-2	3	R32	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3	1968-2029	Dublin	Grass	32	A	1968-07-08	273	100084	NaN	NaN	Tom Okker	R	178.0	NED	24.375086	209534	NaN	NaN	Unknown Fearmon	U	NaN	NaN	NaN	6-1 6-1	3	R32	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
4	1968-2029	Dublin	Grass	32	A	1968-07-08	274	100132	NaN	NaN	Armistead Neely	R	NaN	USA	21.305955	209533	NaN	NaN	Harry Sheridan	U	NaN	IRL	NaN	6-2 6-4	3	R32	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2722	2021-M-DC-2021-WG2-M-THA-DEN-01	Davis Cup WG2 R1: THA vs DEN	Hard	4	D	2021-09-17	4	200416	NaN	NaN	August Holmgren	R	NaN	DEN	23.394935	106397	NaN	NaN	Wishaya Trongcharoenchaikul	R	NaN	THA	26.433949	6-4 6-4	3	RR	90.0	3.0	3.0	52.0	29.0	22.0	16.0	10.0	0.0	1.0	8.0	2.0	79.0	45.0	30.0	14.0	10.0	10.0	13.0	905.0	16.0	767.0	27.0
2723	2021-M-DC-2021-WG2-M-THA-DEN-01	Davis Cup WG2 R1: THA vs DEN	Hard	4	D	2021-09-17	5	208937	NaN	NaN	Kasidit Samrej	R	NaN	THA	20.629706	134087	NaN	NaN	Johannes Ingildsen	R	NaN	DEN	24.202601	7-5 6-3	3	RR	88.0	6.0	6.0	58.0	28.0	21.0	13.0	11.0	3.0	7.0	5.0	8.0	76.0	43.0	24.0	13.0	10.0	5.0	11.0	1136.0	8.0	1546.0	2.0
2724	2021-M-DC-2021-WG2-M-TUR-LAT-01	Davis Cup WG2 R1: TUR vs LAT	Hard	4	D	2021-09-18	1	123795	NaN	NaN	Altug Celikbilek	U	NaN	TUR	25.015743	207669	NaN	NaN	Robert Strombachs	U	NaN	GER	21.995893	7-6(4) 6-4	3	RR	124.0	6.0	8.0	80.0	34.0	25.0	23.0	11.0	6.0	8.0	3.0	5.0	97.0	50.0	34.0	20.0	11.0	6.0	9.0	170.0	427.0	671.0	38.0
2725	2021-M-DC-2021-WG2-M-TUR-LAT-01	Davis Cup WG2 R1: TUR vs LAT	Hard	4	D	2021-09-18	2	117356	NaN	NaN	Cem Ilkel	R	185.0	TUR	26.064339	105208	NaN	NaN	Ernests Gulbis	R	190.0	LAT	33.037645	6-4 6-1	3	RR	65.0	2.0	2.0	41.0	27.0	23.0	7.0	9.0	0.0	1.0	1.0	5.0	45.0	21.0	16.0	7.0	8.0	2.0	7.0	176.0	408.0	196.0	359.0
2726	2021-M-DC-2021-WG2-M-TUR-LAT-01	Davis Cup WG2 R1: TUR vs LAT	Hard	4	D	2021-09-18	4	144985	NaN	NaN	Ergi Kirkin	R	NaN	TUR	22.628337	120270	NaN	NaN	Edgars Manusis	U	NaN	LAT	34.557153	6-1 6-2	3	RR	48.0	4.0	4.0	44.0	20.0	18.0	15.0	8.0	1.0	1.0	0.0	1.0	49.0	34.0	17.0	7.0	7.0	3.0	7.0	311.0	166.0	NaN	NaN

	Player	Winrate	Winning Minutes	Winning Aces	Winning Double Fault	Winning Serve Point	Winning First In	Winning First Point Won	Winning Second Point Won	Winning Serve Games	Winning Break Points Saved	Winning Break Points Faced	Losing Minutes	Losing Aces	Losing Double Fault	Losing Serve Point	Losing First In	Losing First Point Won	Losing Second Point Won	Losing Serve Games	Losing Break Points Saved	Losing Break Points Faced
124	Jimmy Connors	0.809131	121.097561	2.317073	2.317073	85.756098	56.487805	38.097561	15.878049	12.951220	4.609756	7.073171	114.631579	1.184211	2.657895	80.447368	51.684211	29.657895	13.078947	11.947368	5.605263	10.657895
187	Alexander Volkov	0.542705	100.187817	5.253521	3.032864	83.948357	44.769953	33.666667	20.887324	12.849765	4.215962	6.267606	95.481250	4.555556	4.333333	83.280702	42.953216	28.929825	17.812865	12.245614	5.374269	9.690058
234	John McEnroe	0.815087	114.483333	6.229508	4.081967	78.196721	44.901639	35.606557	17.590164	12.573770	3.262295	4.836066	119.696970	3.941176	3.970588	81.500000	44.764706	30.705882	16.764706	12.117647	4.852941	8.470588
250	Johan Kriek	0.630182	74.333333	5.666667	2.666667	66.000000	33.000000	26.333333	18.666667	10.000000	2.666667	3.666667	88.000000	4.500000	7.000000	79.375000	43.000000	30.000000	14.875000	11.375000	6.000000	9.875000
256	Ramesh Krishnan	0.525974	87.333333	0.666667	2.133333	63.000000	44.200000	29.333333	10.333333	10.000000	3.000000	5.066667	105.125000	0.625000	2.958333	79.250000	54.208333	32.166667	11.416667	12.291667	5.083333	9.916667
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
877	Lorenzo Sonego	0.496296	120.772727	6.954545	1.606061	79.439394	51.303030	39.621212	15.772727	12.787879	3.045455	4.363636	121.044776	5.432836	2.343284	82.776119	53.149254	36.492537	13.865672	12.597015	4.432836	7.611940
878	Felix Auger Aliassime	0.573864	113.760000	9.820000	3.730000	75.420000	49.020000	38.670000	14.290000	12.370000	2.830000	4.030000	117.864865	7.027027	5.135135	78.918919	49.108108	33.932432	13.283784	12.283784	5.000000	8.635135
879	Ugo Humbert	0.512605	111.137931	7.931034	2.689655	78.689655	49.465517	38.586207	16.275862	12.620690	3.327586	4.603448	125.357143	7.607143	3.392857	90.821429	54.714286	39.214286	17.553571	14.071429	5.160714	8.464286
880	Miomir Kecmanovic	0.470085	102.407407	4.981481	2.333333	73.796296	44.240741	33.740741	16.796296	12.000000	3.129630	4.703704	106.500000	3.459016	2.540984	81.491803	49.360656	31.950820	15.836066	11.950820	5.622951	9.377049
881	Jannik Sinner	0.653226	106.162500	4.564103	1.820513	74.576923	45.000000	33.217949	16.538462	11.782051	4.076923	5.641026	118.581395	3.465116	2.348837	82.325581	49.255814	32.581395	15.976744	12.372093	4.767442	8.604651

Dep. Variable:	Winrate	R-squared:	0.216
Model:	OLS	Adj. R-squared:	0.187
Method:	Least Squares	F-statistic:	7.400
Date:	Sun, 27 Feb 2022	Prob (F-statistic):	1.12e-18
Time:	22:21:13	Log-Likelihood:	566.48
No. Observations:	559	AIC:	-1091.
Df Residuals:	538	BIC:	-1000.
Df Model:	20
Covariance Type:	nonrobust

	coef	std err	t	P>\|t\|	[0.025	0.975]
const	0.1040	0.110	0.946	0.345	-0.112	0.320
Winning Minutes	-0.0027	0.001	-2.545	0.011	-0.005	-0.001
Winning Aces	-0.0033	0.005	-0.603	0.547	-0.014	0.007
Winning Double Fault	-0.0171	0.009	-1.820	0.069	-0.036	0.001
Winning Serve Point	0.0042	0.007	0.576	0.565	-0.010	0.018
Winning First In	-0.0037	0.005	-0.712	0.477	-0.014	0.007
Winning First Point Won	-0.0051	0.012	-0.432	0.666	-0.028	0.018
Winning Second Point Won	-0.0078	0.014	-0.577	0.564	-0.034	0.019
Winning Serve Games	0.0381	0.029	1.337	0.182	-0.018	0.094
Winning Break Points Saved	0.0305	0.034	0.895	0.371	-0.036	0.098
Winning Break Points Faced	-0.0368	0.033	-1.109	0.268	-0.102	0.028
Losing Minutes	0.0053	0.001	4.843	0.000	0.003	0.007
Losing Aces	0.0003	0.007	0.043	0.966	-0.013	0.014
Losing Double Fault	0.0148	0.008	1.765	0.078	-0.002	0.031
Losing Serve Point	-0.0113	0.009	-1.266	0.206	-0.029	0.006
Losing First In	-0.0023	0.006	-0.410	0.682	-0.013	0.009
Losing First Point Won	0.0264	0.017	1.553	0.121	-0.007	0.060
Losing Second Point Won	0.0174	0.018	0.977	0.329	-0.018	0.052
Losing Serve Games	-0.0335	0.034	-0.982	0.327	-0.100	0.033
Losing Break Points Saved	-0.0896	0.049	-1.836	0.067	-0.185	0.006
Losing Break Points Faced	0.0763	0.047	1.634	0.103	-0.015	0.168

Omnibus:	31.808	Durbin-Watson:	1.650
Prob(Omnibus):	0.000	Jarque-Bera (JB):	35.940
Skew:	0.578	Prob(JB):	1.57e-08
Kurtosis:	3.453	Cond. No.	6.03e+03

Omnibus:	33.206	Durbin-Watson:	1.658
Prob(Omnibus):	0.000	Jarque-Bera (JB):	37.962
Skew:	0.587	Prob(JB):	5.71e-09
Kurtosis:	3.503	Cond. No.	4.98e+03

Omnibus:	25.389	Durbin-Watson:	1.711
Prob(Omnibus):	0.000	Jarque-Bera (JB):	27.590
Skew:	0.527	Prob(JB):	1.02e-06
Kurtosis:	3.269	Cond. No.	1.79e+03